Organizing Committee
 Marta D'Elia
Pasteur Labs. and Stanford University  Johnny Guzman
Brown University  Brittany Hamfeldt
New Jersey Institute of Technology  Michael Neilan
University of Pittsburgh  Maxim Olshanskiy
University of Houston  Sara Pollock
University of Florida  Abner Salgado
University of Tennessee  Valeria Simoncini
Università di Bologna
Abstract
This Semester Program will bring together both leading experts and junior researchers to discuss the current stateoftheart and emerging trends in computational PDEs. While there are scores of numerical methodologies designed for a wide variety of PDEs, the program will be designed around three workshops each centered around a specific theme: PDEs and Geometry, Nonlocal PDEs, and Numerical Analysis of Multiphysics problems. This grouping of topics embodies a broad representation of computational mathematics with each set possessing its own skill set of mathematical tools and viewpoints. Nonetheless, all workshops will have the common theme of using rigorous mathematical theory to develop and analyze the convergence and efficiency of numerical methods. The diversity of the workshop topics will bring together historically distinct groups of mathematicians to interact and facilitate new ideas and breakthroughs.
Confirmed Speakers & Participants
Talks will be presented virtually or inperson as indicated in the schedule below.
 Speaker
 Poster Presenter
 Attendee
 Virtual Attendee

Pedro Aceves Sanchez
University of ArizonaApr 1420, 2024

James Adler
Tufts UniversityJan 29May 4, 2024

Måns Andersson
KTH Royal Institute of TechnologyMar 1115, 2024

Anca Andrei
Tufts UniversityFeb 1216, 2024; Mar 1115, 2024

Daniel Appelö
Virginia TechJan 29Mar 31, 2024

Douglas Arnold
University of MinnesotaMar 330, 2024

Gerard Awanou
University of Illinois, ChicagoMar 1115, 2024

blanca ayuso de dios
universita milanobicoccaMar 31May 4, 2024

Francis Aznaran
University of Notre DameFeb 1216, 2024

Gabriel Barrenechea
University of StrathclydeFeb 10Mar 10, 2024

Soeren Bartels
University of FreiburgMar 727, 2024

Yuri Bazilevs
Brown UniversityApr 24, 2024

Alex Bespalov
University of BirminghamApr 121, 2024

Florin Bobaru
UNIVERSITY OF NEBRASKA–LINCOLNApr 1519, 2024

Andrea Bonito
Texas A&M UniversityApr 1519, 2024

Juan Pablo Borthagaray
Universidad de la República, UruguayApr 1519, 2024

Lucas Bouck
Carnegie Mellon UniversityMar 1115, 2024

Susanne Brenner
Louisiana State UniversityFeb 1Apr 30, 2024

Sylvie Bronsard
New York UniversityFeb 1216, 2024

Jed Brown
University of Colorado BoulderFeb 1216, 2024

Martina Bukač
University of Notre DameFeb 1216, 2024

Olena Burkovska
Oak Ridge National LaboratoryApr 1519, 2024

Erik Burman
University College LondonMar 1016, 2024

Suncica Canic
University of California, BerkeleyFeb 1216, 2024

John Carter
Rensselaer Polytechnic InstituteJan 22May 31, 2024

Casey Cavanaugh
Louisiana State UniversityJan 28May 3, 2024

Zheng “Leslie” Chen
University of Massachusetts DartmouthJan 29May 3, 2024

Yanlai Chen
University of Massachusetts, DartmouthFeb 1216, 2024; Mar 1115, 2024; Apr 1519, 2024

Yingda Cheng
Virginia TechFeb 26Mar 9, 2024

Brian Choi
United States Military AcademyApr 1519, 2024

Giovanna Citti
university of BolognaMar 1115, 2024

Alessandro Contri
NTNU  Norwegian University of Science and TechnologyMar 1115, 2024

Melissa De Jesus
Florida International UniversityApr 1519, 2024

Diego DelCastilloNegrete
ORNLApr 1519, 2024

Alan Demlow
Texas A&M UniversityMar 1115, 2024

Amanda Diegel
Mississippi State UniversityMar 1115, 2024

Wei Ding
Purdue UniversityApr 1519, 2024

Nadejda Drenska
Louisiana State UniversityMar 1115, 2024

Vladimir Druskin
Worcester Polytechnic InstituteFeb 129, 2024

Qiang Du
Columbia UniversityFeb 1216, 2024; Mar 1013, 2024; Apr 1519, 2024

Rebecca Durst
University of PittsburghFeb 1216, 2024

John Evans
University of Colorado BoulderFeb 1216, 2024

Amara Eze
Morgan State UniversityApr 1519, 2024

Thomas Fai
Brandeis UniversityMar 1115, 2024

Tyler Fara
Oregon State UniversityFeb 1216, 2024; Mar 1115, 2024

Patrick Farrell
University of OxfordFeb 49, 2024; Feb 1216, 2024; Mar 1115, 2024

Cynthia Flores
California State University, Channel IslandsApr 1519, 2024

Daniel Fortunato
Flatiron InstituteMar 1115, 2024

Guosheng Fu
University of Notre DameFeb 11Apr 30, 2024

Padi Fuster Aguilera
University of Colorado BoulderMar 1115, 2024

Tom Gilat
BarIlan UniversityMar 1115, 2024

Christian Glusa
Sandia National LabApr 1220, 2024

Sônia Gomes
Universidade Estadual de CampinasJan 29Apr 26, 2024

Hector Gomez
Purdue UniversityFeb 1216, 2024

Tristan Goodwill
University of ChicagoJan 28Mar 16, 2024

Jay Gopalakrishnan
Portland State UniversityMar 1022, 2024

Yuliya Gorb
NSFApr 1519, 2024

Boyce Griffith
University of North Carolina at Chapel HillFeb 1216, 2024

Johnny Guzman
Brown UniversityJan 29May 4, 2024

Sammy Habach
University of Rhode IslandApr 1519, 2024

Daozhi Han
State University of New York at BuffaloApr 1May 3, 2024

Zhaolong Han
University of California San DiegoApr 1420, 2024

Xiaoming He
Missouri University of Science and TechnologyFeb 1216, 2024

Yunhui He
University of HoustonFeb 1216, 2024

Qingguo Hong
Missouri University of Science and TechnologyApr 1420, 2024

Tamas Horvath
Oakland UniversityFeb 1216, 2024; Apr 1724, 2024

Kaibo Hu
University of EdinburghMar 27Apr 2, 2024

Xiaozhe Hu
Tufts UniversityJan 28May 4, 2024

Juntao Huang
Texas Tech UniversityFeb 1216, 2024; Apr 1519, 2024

Xiaokai Huo
Iowa State UniversityFeb 1116, 2024

Olaniyi Iyiola
Morgan State UniversityApr 1420, 2024

Siavash Jafarzadeh
Lehigh UniversityApr 1519, 2024

Gabriela Jaramillo
University of HoustonApr 1519, 2024

Ricardo Kabila
University of Massachusetts DartmouthFeb 20May 3, 2024; Apr 1519, 2024

Brendan Keith
Brown UniversityMar 1115, 2024

Arkadz Kirshtein
Tufts UniversityFeb 1116, 2024; Mar 1115, 2024; Apr 1519, 2024

Natalia Kopteva
University of LimerickFeb 1117, 2024; Apr 1519, 2024

Balázs Kovács
University of PaderbornMar 1115, 2024

Dmitri Kuzmin
Technische Universität DortmundFeb 1216, 2024

Rongjie Lai
Purdue UniversityMar 1115, 2024

Jeonghun Lee
Baylor UniversityFeb 1216, 2024

KangJu Lee
Seoul National UniversityApr 1420, 2024

Hyesuk Lee
Clemson UniversityFeb 1216, 2024

SANGHYUN LEE
Florida State UniversityFeb 1216, 2024

Seulip Lee
University of GeorgiaFeb 1216, 2024

Satchel Lefebvre
TuftsFeb 1216, 2024

Dmitriy Leykekhman
University of ConnecticutApr 1519, 2024

Jichun Li
University of Nevada Las VegasFeb 1117, 2024; Apr 1520, 2024

Xingjie Li
University of North Carolina at CharlotteFeb 1216, 2024; Mar 1115, 2024; Apr 1519, 2024

Daozhe Lin
Columbia UniversityApr 1419, 2024

Guang Lin
Purdue UniversityApr 1519, 2024

Yuan Liu
Wichita State UniversityJan 31May 4, 2024

Sijing Liu
Brown UniversitySep 1, 2023May 31, 2024

WANGBO LUO
The George Washington UniversityApr 1519, 2024

Erdogan Madenci
University of ArizonaApr 1519, 2024

Rees Madsen
Northeastern UniversityMar 1115, 2024

Alexandre Madureira
Laboratório Nacional de Computação CientíficaMar 19May 3, 2024

Anotida Madzvamuse
The University of British ColumbiaFeb 1216, 2024

L Mahadevan
Harvard UniversityMar 1115, 2024

Georg Maierhofer
University of CambridgeApr 920, 2024

Frederic Marazzato
University of ArizonaApr 1519, 2024

KentAndre Mardal
University of OsloFeb 8Apr 18, 2024

Marissa Masden
ICERMSep 6, 2023May 31, 2024

Andre Massing
Norwegian University of Technology and ScienceMar 1115, 2024

Praveeni Mathangadeera
Oregon State UniversityFeb 1216, 2024

Leticia Mattos Da Silva
Massachusetts Institute of TechnologyMar 1115, 2024

Patrick McDonough
California State University Channel IslandsApr 1519, 2024

Amnon Meir
Southern Methodist UniversityFeb 1216, 2024; Apr 1420, 2024

JeanMarie Mirebeau
ENS ParisSaclay, CNRS, Université ParisSaclayMar 1115, 2024

Monica MoralesHernandez
Adelphi UniversityFeb 1214, 2024

Michael Neilan
University of PittsburghFeb 1216, 2024; Mar 1115, 2024; Apr 1519, 2024

Nilima Nigam
Simon Fraser UniversityMar 1115, 2024

Ricardo Nochetto
University of MarylandJan 28May 3, 2024

Eoghan O'Keefe
Tufts UniversityFeb 1216, 2024

Maxim Olshanskiy
University of HoustonFeb 1Apr 30, 2024

Michael Parks
Oak Ridge National LaboratoryApr 1519, 2024

Ravi Patel
Sandia National LaboratoriesJan 31Feb 3, 2024

abani patra
ABANI K PATRAFeb 1216, 2024

Malgorzata Peszynska
Oregon State UniversityFeb 1216, 2024; Mar 1115, 2024; Apr 1519, 2024

Rodrigo Platte
Arizona State UniversityApr 1519, 2024

Sara Pollock
University of FloridaJan 28May 4, 2024

Annalisa Quaini
University of HoustonFeb 1216, 2024

Petronela Radu
University of Nebraska, LincolnApr 1519, 2024

Sivaguru Ravindran
University of Alabama in HuntsvilleFeb 1216, 2024; Apr 1420, 2024

Leo Rebholz
Clemson UniversityJan 28May 3, 2024

Arnold Reusken
Aachen UniversityMar 1115, 2024

Beatrice Riviere
Rice UniversityFeb 417, 2024

Michele Ruggeri
University of BolognaFeb 317, 2024

Ricardo RuizBaier
Monash UniversityFeb 1216, 2024

Riccardo Sacco
Politecnico di MilanoFeb 1116, 2024

Abner Salgado
University of TennesseeJan 28May 4, 2024

Marcus Sarkis
Worcester Polytechnic InstituteJan 29May 4, 2024

Ridgway Scott
University of ChicagoJan 28May 3, 2024

Guglielmo Scovazzi
Duke UniversityFeb 1216, 2024

Pablo Seleson
Oak Ridge National Laboratory (ORNL)Apr 1116, 2024

Jeremy Shahan
Louisiana State UniversityMar 1115, 2024

Mansur Shakipov
University of Maryland, College ParkMar 1115, 2024

Jinye Shen
Southwestern University of Finance and EconomicsApr 1519, 2024

Joshua Siktar
University of TennesseeKnoxvilleApr 1519, 2024

Valeria Simoncini
Università di BolognaJan 28Feb 28, 2024; Apr 1519, 2024

Gieri Simonett
Vanderbilt UniversityMar 1115, 2024

Tatyana Sorokina
Towson State UniversityMar 1115, 2024

Giselle Sosa Jones
Oakland UniversityFeb 1216, 2024; Apr 1724, 2024

Reed Spitzer
Brown UniversityApr 1519, 2024

Monica Stephens
Spelman CollegeJan 29May 3, 2024

Ari Stern
Washington University in St. LouisMar 1115, 2024

Zheng Sun
The University of AlabamaJan 29May 3, 2024

Liyeng Sung
Louisiana State UniversityJan 30Apr 30, 2024

Xiaochuan Tian
University of CaliforniaSan DiegoApr 1420, 2024

Giordano Tierra Chica
University of North TexasJan 31May 4, 2024

Nathaniel Trask
Sandia National LaboratoryFeb 1216, 2024

Richard Tsai
University of TexasMar 3Apr 18, 2024

Tabea Tscherpel
Technical University of DarmstadtMar 125, 2024

Chidera Ugbonta
Tufts UniversityApr 1519, 2024

Odirachukwunma Ugwu
Morgan State UniversityApr 1519, 2024

Duygu Vargun
Oak Ridge National LaboratoryFeb 1216, 2024

Alessandro VENEZIANI
Emory UniversityFeb 1216, 2024

Arjun Vijaywargiya
University of Notre DameMar 1016, 2024

Axel Voigt
Institute of Scientific Computing  Technische Universitat DresdenMar 1115, 2024

Henry von Wahl
Friedrich Schiller University JenaJan 28May 4, 2024

Johan Waernegaard
Columbia UniversityMar 1115, 2024

Shawn Walker
Louisiana State UniversityMar 1115, 2024

Noel Walkington
Carnegie Mellon UniversityJan 28May 4, 2024

Ying Wang
University of OklahomaFeb 1116, 2024; Apr 1519, 2024

Christopher Wang
Cornell UniversityJan 28May 4, 2024

Yongxing Wang
University of LeedsMar 1115, 2024

Hong Wang
University of South CarolinaApr 1420, 2024

Xue Wang
Tufts UniversityFeb 1216, 2024

Franziska Weber
Carnegie Mellon UniversityMar 1115, 2024

Ketahene Gamladdalage Madushi Uththara Wickramasinghe
Morgan State UniversityApr 1519, 2024

Carol Woodward
Lawrence Livermore National LaboratoryFeb 1116, 2024

Bobbie Wu
University of Massachusetts LowellMar 1115, 2024

Zhongqin Xue
Tufts UniversityFeb 1216, 2024

Qihao Ye
University of California, San DiegoApr 1419, 2024

SonYoung Yi
University of Texas at El PasoFeb 1216, 2024

Yue Yu
Lehigh UniversityFeb 1216, 2024; Apr 1519, 2024

Yukun Yue
University of Wisconsin, MadisonJan 29May 31, 2024

Sara Zahedi
KTH Royal Institute of TechnologyMar 1115, 2024

Yanzhi Zhang
Missouri University of Sciences and TechnologyApr 1519, 2024

Xu Zhang
Oklahoma State UniversityFeb 1216, 2024

Yanxiang Zhao
George Washington UniversityApr 1618, 2024

Hongkai Zhao
Duke UniversityMar 1115, 2024

Haoyang Zheng
Purdue UniversityApr 1519, 2024

Qiao Zhuang
Worcester Polytechnic InstituteFeb 1216, 2024

Ludmil Zikatanov
The Pennsylvania State UniversityApr 1519, 2024
Visit dates listed on the participant list may be tentative and subject to change without notice.
Semester Schedule
Monday, January 29, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:00 am  4:30 pm ESTCheck In11th Floor Collaborative Space

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Tuesday, January 30, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:30  10:00 am ESTICERM Director and Organizer WelcomeWelcome  11th Floor Lecture Hall
 Johnny Guzman, Brown University
 Brendan Hassett, ICERM/Brown University
 Maxim Olshanskiy, University of Houston
 Sara Pollock, University of Florida
 Abner Salgado, University of Tennessee
 Valeria Simoncini, Università di Bologna

12:00  1:00 pm ESTPostdoc/Graduate Student Meeting with ICERM DirectorMeeting  11th Floor Conference Room

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Wednesday, January 31, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

10:00 am  12:00 pm ESTPostdoc/ Grad IntroductionsLightning Talks  11th Floor Lecture Hall
 John Carter, Rensselaer Polytechnic Institute
 Casey Cavanaugh, Louisiana State University
 Tristan Goodwill, University of Chicago
 Sijing Liu, Brown University
 Marissa Masden, ICERM
 Henry von Wahl, Friedrich Schiller University Jena
 Christopher Wang, Cornell University
 Yukun Yue, University of Wisconsin, Madison

2:00  3:00 pm ESTInformal TeaCoffee Break  11th Floor Collaborative Space
Thursday, February 1, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:00  9:45 am ESTIntroduction and TensorFlow basicsTutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories

9:45  10:15 am ESTNeural networks ITutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories

10:15  10:30 am ESTBreakCoffee Break

10:30  11:30 am ESTNeural networks IITutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories

11:30 am  1:30 pm ESTLunch/Free Time

1:30  4:00 pm ESTPhysics informed neural networks and inverse problemsTutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories

4:00  4:30 pm ESTCoffee Break11th Floor Collaborative Space

4:30  5:00 pm ESTBayesian inference and Gaussian Processes ITutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories
Friday, February 2, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:00  10:15 am ESTBayesian Inference and Gaussian Processes IITutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories

10:15  10:30 am ESTBreakCoffee Break

10:30  11:30 am ESTOperator Learning ITutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories

11:30 am  1:30 pm ESTLunch/Free Time

1:30  3:30 pm ESTOperator Learning IITutorial  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space

4:00  4:30 pm ESTAdvanced topics. Future directions. Reproducibility.Tutorial  11th Floor Lecture Hall

4:30  5:00 pm ESTOpen Problem SessionProblem Session  11th Floor Lecture Hall
 Ravi Patel, Sandia National Laboratories
Monday, February 5, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

8:30  10:30 am ESTPoisson equation with Dirichlet conditionsTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: switch to geometric multigrid
exercise: switch to highorder 
10:30  10:45 am ESTBreakCoffee Break  11th Floor Collaborative Space

10:45 am  12:45 pm ESTMeshes and meshingTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: adaptive discretisation on Lshaped domain

12:45  2:30 pm ESTLunch/Free Time

2:30  4:30 pm ESTPoisson with Neumann and Robin conditionsTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Tuesday, February 6, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

8:30  10:30 am ESTA foray into transient PDEsTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: CrankNicolson for the heat equation
exercise: implicit RK discretisation of the heat equation 
10:30  10:45 am ESTBreakCoffee Break  11th Floor Collaborative Space

10:45 am  12:45 pm ESTMixed formulations: the Stokes equationsTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: nonNewtonian Stokes

12:45  2:30 pm ESTLunch/Free Time

1:00  2:00 pm ESTPost Doc/Graduate Student Seminar11th Floor Conference Room

2:30  4:30 pm ESTCompressible hyperelasticityTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: write a solver from scratch

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Wednesday, February 7, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

8:30  10:30 am ESTVariational inequalities: the obstacle problemTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: apply a multilevel solver

10:30  10:45 am ESTBreakCoffee Break  11th Floor Collaborative Space

10:45 am  12:45 pm ESTEigenvalue problemsTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: recreate MATLAB logo

12:45  2:30 pm ESTLunch/Free Time

2:30  4:30 pm ESTBlock preconditioners: Stokes and NavierStokesTutorial  11th Floor Lecture Hall
 Patrick Farrell, University of Oxford
Abstract
exercise: Reynoldsrobust solvers for NavierStokes

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Thursday, February 8, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:00  3:30 pm ESTInformal discussion on computational linear algebraDiscussion  11th Floor Lecture Hall
 Valeria Simoncini, Università di Bologna

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Friday, February 9, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space

4:00  4:30 pm ESTDiscontinuous Galerkin for Moist Air with Implicit Condensation11th Floor Lecture Hall
 Henry von Wahl, Friedrich Schiller University Jena
Monday, February 12, 2024

8:30  8:50 am ESTCheck In11th Floor Collaborative Space

8:50  9:00 am ESTWelcome11th Floor Lecture Hall
 Brendan Hassett, ICERM/Brown University

9:00  9:45 am ESTNumerical modeling of fluidstructure interaction11th Floor Lecture Hall
 Speaker
 Martina Bukač, University of Notre Dame
 Session Chair
 Sara Pollock, University of Florida
Abstract
Fluidstructure interaction problems arise in many applications. In biomedicine, such models are used to describe the interaction between blood and arterial walls. Other applications include geomechanics and aerodynamics. When a deformable structure is porous and allows flow through it, poroelastic models are commonly used to describe its behavior. The numerical simulation of fluidelastic/poroelastic structure interaction problems has received considerable attention, but still remains a significant challenge in the mathematical and computational sciences. Main difficulties stem from the the intricate multiphysics nature of the problem, and strong nonlinearities. In this talk, we will present some recent advances in numerical modeling of fluidstructure interaction problems, including adaptive, partitioned methods where the domain movement is handled using an Arbitrary LagragianEulerian approach, and a fixed mesh scheme based on the diffuse interface method. We will also present an application of solvers for fluidstructure interaction in the design of a bioartifical pancreas.

10:00  10:30 am ESTCoffee Break11th Floor Collaborative Space

10:30  11:15 am ESTOn the design and analysis of propertypreserving finite element schemes for hyperbolic problems11th Floor Lecture Hall
 Speaker
 Dmitri Kuzmin, Technische Universität Dortmund
 Session Chair
 Sara Pollock, University of Florida
Abstract
This talk presents a family of algebraically constrained finite element schemes for hyperbolic conservation laws. The validity of (generalized) discrete maximum principles is enforced using monolithic convex limiting (MCL), a new flux correction procedure based on representation of spatial semidiscretizations in terms of admissible intermediate states. Semidiscrete entropy stability is enforced using a limiterbased fix. Time integration is performed using explicit or implicit RungeKutta methods, which can also be equipped with propertypreserving flux limiters. In MCL schemes for nonlinear systems, problemdependent inequality constraints are imposed on scalar functions of conserved variables to ensure physical and numerical admissibility of approximate solutions. After explaining the design philosophy behind our fluxcorrected finite element approximations and showing some numerical examples, we turn to the analysis of consistency and convergence. In particular, we prove a LaxWendrofftype theorem for the inequalityconstrained semidiscrete problem. A key component of our analysis is the use of a weak estimate on bounded variation, which follows from the semidiscrete entropy stability property of the method under investigation. For the Euler equations of gas dynamics, we prove weak convergence to a dissipative weak solution. The convergence analysis to be presented in this talk is joint work with Maria LukáčováMedvid’ová and Philipp Öffner.

11:30 am  12:15 pm ESTCoupling mechanics with biochemistry to understand single and collective cell migration: A geometric bulksurface partial differential equation approach11th Floor Lecture Hall
 Speaker
 Anotida Madzvamuse, The University of British Columbia
 Session Chair
 Sara Pollock, University of Florida
Abstract
In this talk I will present a geometric bulksurface partial differential equations (GBSPDEs) approach for coupling mechanics with biochemistry to understand mechanisms for single and collective cell migration. The GBSPDEs are solved efficiently using tailormade numerical methods depending on the properties of the mathematical models; two novel numerical methods will be presented: (i) the evolving bulksurface finite element method suitable for solving GBSPDEs on evolving domains and manifolds for sharpinterface formulations and (ii) the geometric multigrid method suitable for solving PDEs on evolving domains and manifolds using diffuse interface formulations. Experimentally driven inspired applications will be presented, demonstrating the novelty, applicability and generality of this mechanobiochemical modelling approach to studying single and collective cell migration. Cell migration is essential for many physiological and pathological processes. It plays a central role in the development and maintenance of multicellular organisms. Tissue formation during embryonic development, wound healing and immune responses as well as the formation of cancer, all require the orchestrated movement of cells in particular directions to specific locations. Hence, understanding single cell dynamics during movement is critically important in development, biomedicine and biomedical engineering.

12:30  2:30 pm ESTLunch/Free Time

2:30  3:15 pm ESTConvergence of numerical methods for coupled CahnHilliard and NavierStokes equations11th Floor Lecture Hall
 Speaker
 Beatrice Riviere, Rice University
 Session Chair
 Sara Pollock, University of Florida
Abstract
Modeling multicomponent flows in porous media is important for many applications relevant to energy and environment. Advances in porescale imaging, increasing availability of computational resources, and developments in numerical algorithms have started rendering direct porescale numerical simulations of multiphase flow in pore structures feasible. This talk presents recent advances in the discretization of phasefield models for systems of twophase flows. Spatial discretization is based on the interior penalty discontinuous Galerkin methods. Time discretization utilizes a decoupled splitting approach. Both theory and application of the proposed methods to model flows in porous structures are discussed.

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space

4:00  4:45 pm ESTNew optimized RobinRobin domain decomposition methods using Krylov solvers for the StokesDarcy system11th Floor Lecture Hall
 Speaker
 Xiaoming He, Missouri University of Science and Technology
 Session Chair
 Sara Pollock, University of Florida
Abstract
In this presentation, we design optimized Schwarz domain decomposition algorithms to accelerate the Krylov type solution for the StokesDarcy system. We use particular solutions of this system on a circular geometry to analyze the iteration operator mode by mode. We introduce a new optimization strategy of the socalled Robin parameters based on a specific linear relation between these parameters, using the minmax and the expectation minimization approaches. Moreover, we use a Krylov solver to deal with the iteration operator and accelerate this new optimized domain decomposition algorithm. Several numerical experiments are provided to validate the effectiveness of this new method.

5:00  6:30 pm ESTWelcome ReceptionReception  11th Floor Collaborative Space
Tuesday, February 13, 2024

9:00  9:45 am ESTImmersed methods for fluidstructure interactionVirtual
 Virtual Speaker
 Boyce Griffith, University of North Carolina at Chapel Hill
 Session Chair
 Amnon Meir, Southern Methodist University
Abstract
The immersed boundary (IB) method is a framework for modeling systems in which an elastic structure interacts with a viscous incompressible fluid. The fundamental feature of the IB approach to such fluidstructure interaction (FSI) problems is its combination of an Eulerian formulation of the momentum equation and incompressibility constraint with a Lagrangian description of the structural deformations and resultant forces. In conventional IB methods, Eulerian and Lagrangian variables are linked through integral equations with Dirac delta function kernels, and these singular kernels are replaced by regularized delta functions when the equations are discretized for computer simulation. This talk will focus on three related extensions of the IB method. I first detail an IB approach to structural models that use the framework of largedeformation nonlinear elasticity. I will focus on efficient numerical methods that enable finite element structural models in largescale simulations, with examples focusing on models of the heart and its valves. Next, I will describe an extension of the IB framework to simulate soft material failure using peridynamics, which is a nonlocal structural mechanics formulation. Numerical examples demonstrate constitutive correspondence with classical mechanics for nonfailure cases along with essentially gridindependent predictions of fluiddriven soft material failure. Finally, I will introduce a reformulation of the IB largedeformation elasticity framework that enables accurate and efficient fluidstructure coupling through a version of the immersed interface method, which is a sharpinterface IBtype method. Computational examples demonstrate the ability of this methodology to simulate a broad range of fluidstructure mass density ratios without suffering from artificial added mass instabilities, and to facilitate subgrid contact models. I will also present biomedical applications of the methodology, including models of clot capture by inferior vena cava filters.

10:00  10:30 am ESTCoffee BreakVirtual

10:30  11:15 am ESTAAPicardNewton solver for NavierStokes and related problemsVirtual
 Virtual Speaker
 Leo Rebholz, Clemson University
 Session Chair
 Amnon Meir, Southern Methodist University
Abstract
We consider the composition of AAPicard fixed point iteration with Newton iteration, to create a more robust and stable yet still quadratically convergent solver. We analyze for NavierStokes in cases of small data (sufficient for uniqueness) and large data. Tests for NSE and other PDEs with this solver show a remarkable ability to converge for larger Reynolds number (NSE), Rayleigh number (Boussinesq), Kerr coefficient and refraction index (nonlinear Helmholtz), and so on.

11:30 am  12:15 pm ESTOn Reliability and EconomicsVirtual
 Virtual Speaker
 Jed Brown, University of Colorado Boulder
 Session Chair
 Amnon Meir, Southern Methodist University
Abstract
Data structures and algorithms have changed the relative costs, but few production pipelines have internalized the new economics of simulation. For example, matrixfree invert the marginal cost of high order discretizations, enabling large speedups for engineering workflows. Meanwhile, there is frequent dispute among practitioners of when to apply linearizations and assumptions on physical regime, when to use structured vs unstructured meshes, and many other important design choices in a simulation tool. For singlephysics problems, it is perhaps tractable for analysts to determine (usually a posteriori) when these approximations are valid, but that is more difficult and errorprone for multiphysics problems. We reflect on the computational cost, robustness, and user interface consequences of "simplifying" this decision landscape by embracing fully nonlinear formulations with unstructured meshes and implicit solvers. Via case studies in fluid and structural mechanics, we observe that solvers for more general regimes can have low overhead relative to regimespecific solvers, yet come equipped with diagnostics that provide effective a posteriori indicators of physical regime.

12:30  2:30 pm ESTLunch/Free Time

2:30  3:15 pm ESTFluidporoelastic structure interactionVirtual
 Speaker
 Suncica Canic, University of California, Berkeley
 Session Chair
 Amnon Meir, Southern Methodist University
Abstract
We will discuss some recent results on fluidporoelastic structrure interaction between multilayered poroelastic media and viscous, incompressible fluids.
Wednesday, February 14, 2024

9:00  9:45 am ESTMultiscale/Multiphysics Modeling and Simulation in Ophthalmology11th Floor Lecture Hall
 Virtual Speaker
 Riccardo Sacco, Politecnico di Milano
 Session Chair
 Hyesuk Lee, Clemson University
Abstract
Glaucoma is a multifactorial ocular neuropathology representing the second major cause of irreversible blindness. Elevated intraocular pressure (IOP) is an established risk factor of glaucoma determined by aqueous humor dynamics (AHDyn), the balance among production (Pr), diffusion (Diff) and drainage (Dr) of aqueous humor (AH), a watery transparent fluid including electrolytes and low protein concentration. Reducing AHPr and/or increasing AHDr are possible approaches to reduce IOP. In this talk we illustrate a multiscale/multiphysics approach to develop a computational virtual laboratory (CVL) for the simulation of AHDyn based on a 3Dto0D reduction of (1) 3D VelocityExtended PoissonNernstPlanck PDE system to model AHPr; (2) 3D diffusion bulk flow to model AHDiff; (3) 3D poroelastic PDE system to model AHDr. Model variables are the compartment values of electric potential, ion molar densities and fluid pressure and are numerically determined by a fixedpoint iteration which transforms AHDyn simulation into the successive solution of two nonlinear systems of algebraic equations, representing mass balance in AHPr and in AHDiff + AHDr, respectively. Computational tests suggest that Na+/K+ pump and TM/Uv hydraulic facilities are the main biomarkers of a pathological increase in IOP. These results support the potential use of a CVL to assist and optimize the design of IOP lowering medications.

10:00  10:30 am ESTCoffee Break11th Floor Collaborative Space

10:30  11:15 am ESTMultiphysics at multiple scales for coupled [TpHM] processes in permafrost soils11th Floor Lecture Hall
 Speaker
 Malgorzata Peszynska, Oregon State University
 Session Chair
 Hyesuk Lee, Clemson University
Abstract
In this joint work with many students and collaborators we describe robust and conservative computational schemes for coupled process of flow, deformation, and energy with phase change (TpHM) in the soils in permafrost regions. The models present challenges due to the free boundary of freezing/thawing, strong dependence of constitutive parameters on the microphysics of TpHM, disparate time scales, and micro and macro heterogeneity. We also discuss how to get the data for the Darcy scale [TpH] ad [HM] models from the models at the porescale by computational upscaling.

11:30 am  12:15 pm ESTMultiphysics problems related to brain clearance, sleep and dementia11th Floor Lecture Hall
 Speaker
 KentAndre Mardal, University of Oslo
 Session Chair
 Hyesuk Lee, Clemson University
Abstract
Recent theories suggest that a fundamental reason for sleep is simply clearance of metabolic waste produced during the activities of the day. In this talk we will present multiphysics problems and numerical schemes that target these applications. In particular, we will be lead from basic applications of neuroscience into multiphysics problems involving Stokes, Biot and fractional solvers at the brainfluid interface.

12:25  12:30 pm ESTGroup Photo (Immediately After Talk)11th Floor Lecture Hall

12:30  2:30 pm EST

2:30  3:15 pm ESTDirect van der Waals Simulation (DVS): Towards Predictive Simulations of Cavitation and Boiling11th Floor Lecture Hall
 Speaker
 Hector Gomez, Purdue University
 Session Chair
 Hyesuk Lee, Clemson University
Abstract
Cavitating flows are ubiquitous in engineering and science. Despite their significance, a number of fundamental problems remain open; and our ability to make quantitative predictions is very limited. The NavierStokesKorteweg equations constitute a fundamental model of cavitation, which has potential for predictive computations of liquidvapor flows, including cavitation inception —one of the most elusive aspects of cavitation. However, numerical simulation of the NavierStokesKorteweg equations is very challenging, and state of the art simulations are limited to very small Reynolds numbers, open flows (no walls), and in most cases, micrometer length scales. The computational challenges emerge from, at least, (a) the dispersive nature of the solutions to the equations, (b) a complicated eigenstructure of the isentropic form of the equations, which limits the use of standard CFD techniques, and (c) the need to resolve the liquidvapor interface, which without special treatment, has a thickness in the order of nanometers. Here, we present Direct van der Waals simulation (DVS), a new approach that permits, for the first time as far as we are aware, largescale simulations of wallbounded flows with large Reynolds numbers. The proposed discretization scheme is a residualbased approach that emanates from the dispersive nature of the equations and outperforms standard stabilization schemes for advectiondominated problems. We feel that this work opens possibilities for predictive simulations of cavitation.

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space

4:00  4:45 pm ESTA datadriven exterior calculus11th Floor Lecture Hall
 Speaker
 Nathaniel Trask, Sandia National Laboratory
 Session Chair
 Hyesuk Lee, Clemson University
Abstract
Despite the recent flurry of work employing machine learning to develop surrogate models to accelerate scientific computation, the "blackbox" underpinnings of current techniques fail to provide the verification and validation guarantees provided by modern finite element methods. In this talk we present a datadriven finite element exterior calculus for developing reducedorder models of multiphysics systems when the governing equations are either unknown or require closure. The framework employs deep learning architectures typically used for logistic classification to construct a trainable partition of unity which provides notions of control volumes with associated boundary operators. This alternative to a traditional finite element mesh is fully differentiable and allows construction of a discrete de Rham complex with a corresponding Hodge theory. We demonstrate how models may be obtained with the same robustness guarantees as traditional mixed finite element discretization, with deep connections to contemporary techniques in graph neural networks. For applications developing digital twins where surrogates are intended to support real time data assimilation and optimal control, we further develop the framework to support Bayesian optimization of unknown physics on the underlying adjacency matrices of the chain complex. By framing the learning of fluxes via an optimal recovery problem with a computationally tractable posterior distribution, we are able to develop models with intrinsic representations of epistemic uncertainty.
Thursday, February 15, 2024

9:00  9:45 am ESTThe Role of Multiphysics Modeling in the Design of Coronary Stents11th Floor Lecture Hall
 Speaker
 Alessandro Veneziani, Emory University
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
Since their introduction in the Eighties, coronary stents have undergone significant design improvements, making them a critical tool for treating severe obstructions. From original BareMetal Stents (BMS) to Drug Eluting Stents (DES) to the most recent experience of Bioresorbable Stents, the design of these scaffolds was minimally supported by mathematical tools. The patientspecific quantitative analysis of stented coronaries is a difficult task for the variety of complex morphologies left by the stent deployment. Therefore, this type of analysis was limited to a minimal number of patients, not compatible with clinical trials. On the other hand, the development and the failure of Brioresorbable Stents clearly pointed out the importance of rigorous quantitative tools in the design of nextgeneration scaffolds. In this talk, we will present recent results in investigating coronary stents based on Applied Mathematics (as opposed to traditional animal models). We will consider in detail (i) the modeling of the elution in a multidomain problem solved by iterative substructuring methods involving simultaneously the lumen, the wall, and the struts of the stents; (2) the impact of the struts on the wall shear stress of a significant number of patients; (3) the consequent role of shape optimization and model order reduction in the design of scaffolds. This journey through a sophisticated combination of data and models will pinpoint the critical role of applied mathematics and scientific computing not only for a basic understanding of the biomechanics of stents but also for the clinical routine and the design of more performing prostheses.

10:00  10:30 am ESTCoffee Break11th Floor Collaborative Space

10:30  11:15 am ESTDomain Agnostic Neural Operators for Multiphysics Problems11th Floor Lecture Hall
 Speaker
 Yue Yu, Lehigh University
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
Over the past several decades, physicsbased Partial Differential Equations (PDEs) have been the cornerstone for modeling multiphysics problems. Traditional numerical methods have been employed to solve these PDEs and various approaches have been proposed to capture the multiphysics interfaces. However, their accuracy and computational feasibility can be compromised when dealing with unknown governing laws or complex interface geometries, such as in the crack propagation, fluid—structure interaction, and heterogeneous material design problems. In this talk, we develop to use datadriven modeling approaches to learn the hidden physics, capture irregular geometries, and provide accelerated predictions. In particular, we introduce domain agnostic Fourier neural operator (DAFNO), which learns the surrogate mapping between loading conditions and the corresponding physical responses with irregular geometries and evolving domains. The key idea is to incorporate a smoothed characteristic function in the integral layer architecture of neural operators, and leverage FFT to achieve rapid computations for evaluating these integrals, in such a way that the geometric information is explicitly encoded in the architecture. Once trained, DAFNO can provide efficient predictions for physical problems under unseen loading scenarios and evolving domain geometries, which makes it especially suitable to handle the complex interfacial problems in multiphysics modeling. To illustrate the applicability of DAFNO in multiphysics problems, we show three examples. Firstly, we consider a brittle material crack propagation problem which features complex domains with topology changes. Then, in the second example we further consider the corrosion induced cracking in reinforced concrete, which is a multiphysics system involving the interactions between diffusion, chemical reaction, mechanical strain, and crack fields. Last but not least, we show that DAFNO can act as an efficient surrogate for the inverse microstructure design of multifunctional metamaterials. These examples highlight the features of DAFNOs in its generalizability, flexibility, and efficiency.

11:30 am  12:15 pm ESTThe Shifted Boundary Method: An embedded approach for computational mechanics11th Floor Lecture Hall
 Speaker
 Guglielmo Scovazzi, Duke University
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
Embedded/immersed/unfitted boundary methods obviate the need for continual remeshing in many applications involving rapid prototyping and design. Unfortunately, many finite element embedded boundary methods are also difficult to implement due to the need to perform complex cell cutting operations at boundaries, and the consequences that these operations may have on the overall conditioning of the ensuing algebraic problems. We present a new, stable, and simple embedded boundary method, named Shifted Boundary Method (SBM), which eliminates the need to perform cell cutting. Boundary conditions are imposed on a surrogate discrete boundary, lying on the interior of the true boundary interface. We then construct appropriate field extension operators by way of Taylor expansions, with the purpose of preserving accuracy when imposing the boundary conditions. We demonstrate the SBM on largescale solid and fracture mechanics problems; thermoelasticity problems; porous media flow problems; incompressible flow problems governed by the NavierStokes equations (also including free surfaces); and problems governed by hyperbolic conservation laws.

12:30  2:30 pm ESTLunch: Classical theory vs machine learning in educationWorking Lunch  11th Floor Collaborative Space

2:30  3:15 pm ESTA FEM for a phasefield model of twophase incompressible surface flow with electrostatic interaction11th Floor Lecture Hall
 Speaker
 Annalisa Quaini, University of Houston
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
We consider a thermodynamically consistent phasefield model of a twophase flow of incompressible viscous fluids with electrostatic interaction. The model allows for a nonlinear dependence of the fluid density on the phasefield order parameter. Driven by applications in biomembrane studies, the model is written for tangential flows of fluids constrained to a surface and consists of (surface) Navier–Stokes–Cahn–Hilliard type equations. We apply an unfitted finite element method to discretize the system and introduce a fully discrete timestepping scheme with the following properties: (i) the scheme decouples the fluid and phasefield equation solvers at each time step, (ii) the resulting two algebraic systems are linear, and (iii) the numerical solution satisfies the same stability bound as the solution of the original system under some restrictions on the discretization parameters. We provide numerical examples to demonstrate the stability, accuracy, and overall efficiency of the approach and provide validation against experimental data.

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space

4:00  4:45 pm ESTPositivitypreserving discretisations in general meshes11th Floor Lecture Hall
 Speaker
 Gabriel Barrenechea, University of Strathclyde
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
In this talk I will present a method that enforces boundpreservation (at the degrees of freedom) of the discrete solution (recently presented in [1]). The method is built by first defining an algebraic projection onto the convex closed set of finite element functions that satisfy the bounds given by the solution of the PDE. Then, this projection is hardwired into the definition of the method by writing a discrete problem posed for this projected part of the solution. Since this process is done independently of the shape of the basis functions, and no result on the resulting finite element matrix is used, this process guarantees boundpreservation independently of the underlying mesh. The core of the talk will be devoted to explaining the main idea in the context of linear (and nonlinear) reactiondiffusion equations. Then, I will explain the main difficulties encountered when extending this method to convectiondiffusion equations, and to a finite element method defined in polytopal meshes. The results in this talk have been carried out in collaboration with Abdolreza Amiri (Strathclyde, UK), Emmanuil Geourgoulis (HeriotWatt, UK and Athens, Greece), Tristan Pryer (Bath, UK), and Andreas Veeser (Milan, Italy). References 1. G.R. Barrenechea, E. Georgoulis, T. Pryer, and A. Veeser, A nodally boundpreserving finite element method. arXiv:2304.01067, IMA Journal on Numerical Analysis, to appear.
Friday, February 16, 2024

9:00  9:45 am ESTQuantum Digital Twins  a numerical methodist’s adventure in the land of quantum computers11th Floor Lecture Hall
 Speaker
 Daniel Appelö, Virginia Tech
 Session Chair
 Valeria Simoncini, Università di Bologna
Abstract
In this talk I will introducing the most basic concepts in quantum computing and describe one type of quantum computing hardware (a transmon) and how it is modeled. We will then outline the computationally challenging tasks that are needed for making a quantum computer run and introduce numerical methods tailored especially for these tasks. Time permitting I will take you on a comprehensive journey through a realworld example involving characterization, control, and experimental validation, showcasing our experiences with a qutrit device within the Lawrence Livermore QUDIT testbed.

10:00  10:30 am ESTCoffee Break11th Floor Collaborative Space

10:30  11:15 am ESTMonolithic and Partitioned FEM for FSI: ALE divergencefree HDG fluid solver + TDNNS structural solver11th Floor Lecture Hall
 Speaker
 Guosheng Fu, University of Notre Dame
 Session Chair
 Valeria Simoncini, Università di Bologna
Abstract
We present novel (highorder) finite element schemes for the fluidstructure interaction (FSI) problem based on an arbitrary LagrangianEulerian divergencefree hybridizable discontinuous Gakerkin (ALE divergencefree HDG) incompressible flow solver, a TangentialDisplacementNormalNormalStress (TDNNS) nonlinear elasticity solver, and a generalized Robin interface condition treatment. Temporal discretization is performed using the highorder backward difference formulas (BDFs). Both monolithic and strongly coupled partitioned fully discrete schemes are obtained. Numerical convergence studies are performed for the flow and elasticity solvers, and the coupled FSI solver, which verify the highorder spacetime convergence of the proposed schemes. Numerical results on classical two dimensional benchmark problems also showed good performance of our proposed methods.

11:30 am  12:15 pm ESTMixed methods for the coupled Stokes/PoissonNernstPlanck equations in Banach spaces11th Floor Lecture Hall
 Speaker
 Ricardo RuizBaier, Monash University
 Session Chair
 Valeria Simoncini, Università di Bologna
Abstract
I will discuss a Banach spacesbased framework and new mixed finite element methods for the numerical solution of the coupled Stokes and PoissonNernsPlanck equations (a nonlinear model describing the dynamics of electrically charged incompressible fluids). The pseudostress tensor, the electric field (rescaled gradient of the potential) and total ionic fluxes are used as new mixed unknowns. The resulting fully mixed variational formulation consists of two saddlepoint problems, each one with nonlinear source terms depending on the remaining unknowns, and a perturbed saddlepoint problem with linear source terms, which is in turn additionally perturbed by a bilinear form. The wellposedness of the continuous formulation is a consequence of a fixedpoint strategy in combination with the Banach theorem, the BabuskaBrezzi theory, the solvability of abstract perturbed saddlepoint problems, and the BanachNecasBabuska theorem. An analogous approach (but using now both the Brouwer and Banach theorems and stability conditions on arbitrary FE subspaces) is employed at the discrete level. A priori error estimates are derived, and examples of discrete spaces that fit the theory, include, e.g., RaviartThomas elements along with piecewise polynomials. Finally, several numerical experiments confirm the theoretical error bounds and illustrate the balancepreserving properties and applicability of the proposed family of methods. This talk is based on joint work with Claudio I. Correa and Gabriel N. Gatica (from CI2MA, Concepcion).

12:30  2:30 pm ESTLunch: NetworkingWorking Lunch  11th Floor Collaborative Space

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Tuesday, February 20, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  12:30 pm ESTde Rham complexPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Casey Cavanaugh, Louisiana State University

12:30  1:00 pm ESTPotential theoryPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Tristan Goodwill, University of Chicago

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Wednesday, February 21, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm ESTDatadriven computation of the interior solutions of LTI PDE problems with unknown coefficients via network realizations of reduced order models11th Floor Lecture Hall
 Vladimir Druskin, Worcester Polytechnic Institute
Abstract
We consider computation of the solutions of linear timeinvariant hyperbolic PDEs with unknown coefficients from the measurements of their multiinput/multioutput (MIMO) transfer functions with limited number of inputs and outputs. Such problems are paramount in remote sensing and other “noninvasive problems”, e.g., radar imaging, seismic exploration and medical applications, where measurements are not available in the interior problem. We first compute an equivalent network approximation using the interpretation on the Lanczos algorithm via Stieltjes continued fraction, and then compute the state solution by embedding this approximation in the underlying PDE using the finitedifference Gaussian quadratures. We show application of this approach to nondestructive acoustic testing and SAR (Synthetic Aperture Radar) imaging . Contributors to different stages of this longterm project have been Jorn Zimmerling, Mikhail Zaslavsky, Shari Moskow, Alexander Mamonov, Liliana Borcea, Elena Cherkaev and Justin Baker.

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Thursday, February 22, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  1:00 pm ESTAdvances in Flight Simulation and Flow Instability11th Floor Lecture Hall
 Ridgway Scott, University of Chicago
Abstract
A new era in flight is emerging that requires a moreeffective simulation strategy. Many modes of transportation are being developed industrially, including airtaxi drones and groundeffect transport. We describe an approach to simulating flight that is based on instabilities in flow and provides a new view of turbulence based on chaotic dynamics of computed flow profiles. The method we use is the ReynoldsOrr definition of instability that is more general than what is commonly used to define flow instability. We show that our results correlate well with what can be observed by both experiment and direct numerical simulation.

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Friday, February 23, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:30  10:30 am ESTEthics IProfessional Development  11th Floor Lecture Hall

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Monday, February 26, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Tuesday, February 27, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  1:00 pm ESTConvectiondominated equationsPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Casey Cavanaugh, Louisiana State University

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Wednesday, February 28, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  1:00 pm ESTRobust Implicit Adaptive Low Rank TimeStepping Methods for Matrix Differential Equations11th Floor Lecture Hall
 Yingda Cheng, Virginia Tech
Abstract
In this talk, we present a new class of implicit rankadaptive schemes for timedependent matrix differential equations. The dynamic low rank approximation (DLRA) is a wellknown technique to capture the dynamic low rank structure based on Dirac–Frenkel timedependent variational principle. In recent years, it has attracted a lot of attention due to its wide applicability. Our schemes are inspired by the threestep procedure used in the rank adaptive version of the unconventional robust integrator (the so called BUG integrator) for DLRA. First, a prediction (basis update) step is made computing the approximate column and row spaces at the next time level. Second, a Galerkin evolution step is invoked using a base implicit solve for the small core matrix. Finally, a truncation is made according to a prescribed error threshold. Since the DLRA is evolving the differential equation projected on to the tangent space of the low rank manifold, the error estimate of the BUG integrator contains the tangent projection (modeling) error which cannot be easily controlled by mesh refinement. This can cause convergence issue for equations with cross terms. To address this issue, we propose a simple modification, consisting of merging the row and column spaces from the explicit step truncation method together with the BUG spaces in the prediction step. In addition, we propose an adaptive strategy where the BUG spaces are only computed if the residual for the solution obtained from the prediction space by explicit step truncation method, is too large. We prove stability and estimate the local truncation error of the schemes under assumptions. We benchmark the schemes in several tests, such as anisotropic diffusion, solid body rotation and the combination of the two, to show robust convergence properties.

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Thursday, February 29, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  1:00 pm ESTFundamentals of (simplicial) Mesh Generation11th Floor Lecture Hall
 Noel Walkington, Carnegie Mellon University

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Friday, March 1, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:30  10:30 am ESTEthics IIProfessional Development  11th Floor Lecture Hall

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Monday, March 4, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Tuesday, March 5, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  12:30 pm ESTDiscontinuous Galerkin methodPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Henry von Wahl, Friedrich Schiller University Jena

12:30  1:00 pm ESTSurface PDEsPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Tristan Goodwill, University of Chicago

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Wednesday, March 6, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Thursday, March 7, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm ESTRungeKutta discontinuous Galerkin methods beyond the method of lines11th Floor Lecture Hall
 Zheng Sun, The University of Alabama
Abstract
In the common practice of the methodoflines approach for discretizing a timedependent partial differential equation (PDE), people first apply spatial discretization to convert the PDE into an ordinary differential equation system. Subsequently, a time integrator is used to discretize the time variable. When a multistage RungeKutta (RK) method is used for time integration, by default, the same spatial operator is used at all RK stages. But what if one allows different spatial operators at different stages? In this talk, we present two of our recent explorations on blending different stage operators in RK discontinuous Galerkin (DG) methods for solving hyperbolic conservation laws. In our first work, we mix the DG operator with the local derivative operator, yielding an RKDG method featuring compact stencils and simple boundary treatment. In our second work, we mix the DG operators with polynomials of degrees k and k1, and the resulting method may allow larger time step sizes and fewer floatingpoint operations per time step.

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Friday, March 8, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:30  10:30 am ESTJob ApplicationsProfessional Development  11th Floor Lecture Hall

11:00 am  12:00 pm ESTFast Integral Equation Method for Surface PDEs11th Floor Lecture Hall
 Tristan Goodwill, University of Chicago

3:30  4:00 pm ESTCoffee Break11th Floor Collaborative Space
Monday, March 11, 2024

8:30  8:50 am EDTCheck In11th Floor Collaborative Space

8:50  9:00 am EDTWelcome11th Floor Lecture Hall
 Brendan Hassett, ICERM/Brown University

9:00  9:45 am EDTA nonlinear leastsquares convexity enforcing finite element method for the MongeAmpere equation11th Floor Lecture Hall
 Speaker
 Susanne Brenner, Louisiana State University
 Session Chair
 Michael Neilan, University of Pittsburgh
Abstract
We present a nonlinear leastsquares finite element method for computing the smooth convex solutions of the Dirichlet boundary value problem of the MongeAmpere equation on smooth strictly convex planar domains. It is based on an isoparametric finite element space with exotic degrees of freedom that can enforce the convexity of the approximate solutions.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTThe Second Boundary Value Problem for a Discrete Monge–Ampere Equation11th Floor Lecture Hall
 Speaker
 Gerard Awanou, University of Illinois, Chicago
 Session Chair
 Michael Neilan, University of Pittsburgh
Abstract
We propose a discretization of the second boundary condition for the Monge–Ampere equation arising in geometric optics and optimal transport. The discretization we propose is the natural generalization of the popular Oliker–Prussner method proposed in 1988. For the discretization of the differential operator, we use a discrete analogue of the subdifferential. Existence and stability of the solutions to the discrete problem are established. Convergence results to the continuous problem are given.

11:30 am  12:15 pm EDTA Volumetric Approach to Monge's Optimal Transport on Surfaces11th Floor Lecture Hall
 Speaker
 Richard Tsai, University of Texas
 Session Chair
 Michael Neilan, University of Pittsburgh
Abstract
In this talk, we present a novel approach for solving the MongeAmpere (MA) equation defined on a sphere. Specifically, we extend the MA equation on a sphere to a narrowband around the sphere by formulating an equivalent optimal transport problem. We demonstrate that the extended MA equation can be solved using existing algorithms developed for the MA equation on Euclidean space, making the resulting algorithm simple and easy to implement. Our approach provides a useful tool for solving problems that involve the MA equation defined on or near a sphere, which has a wide range of applications in fields such as computer graphics, image processing, and fluid dynamics.

12:30  2:30 pm EDTLunch/Free Time

2:30  3:15 pm EDTDiscretizations of anisotropic PDEs using Voronoi's reduction of quadratic forms.11th Floor Lecture Hall
 Speaker
 JeanMarie Mirebeau, ENS ParisSaclay, CNRS, Université ParisSaclay
 Session Chair
 Michael Neilan, University of Pittsburgh
Abstract
Anisotropy, which refers to the existence of preferred direction in a domain, is a source of difficulty in the discretization of partial differential equations (PDEs). For instance, monotone discretization schemes for anisotropic PDEs cannot be strictly local, but need to use wide stencils. When the PDE is discretized over a Cartesian grid domain, one can often leverage a matrix decomposition technique known as Voronoi's first reduction, which helps in finding the best possible compromises in the design of anisotropic finite difference schemes. I will describe this tool and its application to monotone discretizations of HamiltonJacobiBellman PDEs, as well as a recent extensions to the elastic wave equation in a fully general anisotropic medium.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space

4:00  4:45 pm EDTControlling growth and form: mineral, vegetable and animal11th Floor Lecture Hall
 Speaker
 L Mahadevan, Harvard University
 Session Chair
 Michael Neilan, University of Pittsburgh
Abstract
Shape enables and constrains function across scales, in living and nonliving systems. Following a brief introduction to morphogenesis in biology that rapidly touches on how stems, leaves, flowers, bodies, guts, beaks and brains get their shape, I will switch to the inverse problem of how to program and design shape using 3 examples: chemical precipitation, 4d printing and origami/kirigami. Along the way, I will indicate how these pandisciplinary problems provide a plethora of questions in mathematics, physics and biology, with potential implications for technology.

5:00  6:30 pm EDTWelcome ReceptionReception  11th Floor Collaborative Space
Tuesday, March 12, 2024

9:00  9:45 am EDTComputational Meanfield Games: From Conventional Methods to Deep Generative Models11th Floor Lecture Hall
 Speaker
 Rongjie Lai, Purdue University
 Session Chair
 Maxim Olshanskiy, University of Houston
Abstract
Mean field game (MFG) problems study how a large number of similar rational agents make strategic movements to minimize their costs. They have recently gained great attention due to their connection to various problems, including optimal transport, gradient flow, deep generative models, as well as reinforcement learning. In this talk, I will elaborate our recent computational efforts on MFGs. I will start with a lowdimensional setting, employing conventional discretization and optimization methods, delving into the convergence results of our proposed approach. Afterwards, I will extend my discussion to highdimensional problems by bridging the trajectory representation of MFG with a special type of deep generative model—normalizing flows. This connection not only helps solve highdimensional MFGs but also provides a way to improve the robustness of normalizing flows. If time permits, I will further address the extension of these methods to Riemannian manifolds in lowdimensional and higherdimensional setting, respectively.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTSemiSupervised Learning with the pLaplacian in Geometric Methods in Machine Learning and Data Analysis11th Floor Lecture Hall
 Speaker
 Nadejda Drenska, Louisiana State University
 Session Chair
 Maxim Olshanskiy, University of Houston
Abstract
The field of semisupervised learning involves learning from both labeled and unlabeled data. By exploiting the structure of the unlabeled data, such as its geometric or topological properties, semisupervised classifiers can obtain good performance with far fewer labels than are required in fully supervised learning (when classifiers learn only from labeled data). A semisupervised approach is necessary when labels are very expensive to obtain, as is the case in a majority of classification applications, such as website classification, text recognition, protein sequencing, medical imaging, natural language processing. In this talk we apply pLaplacian regularization to cases of very low labeling rate; in such applications this approach classifies properly when the standard Laplacian regularization does not. Using the twoplayer stochastic game interpretation of the pLaplacian, we prove asymptotic consistency of pLaplacian regularized semisupervised learning, thus justifying the utility of the pLaplacian.
This is joint work with Jeff Calder. 
11:30 am  12:15 pm EDTSolving PDEs on point clouds with applications to shape analysis11th Floor Lecture Hall
 Speaker
 Hongkai Zhao, Duke University
 Session Chair
 Maxim Olshanskiy, University of Houston
Abstract
Using point clouds is the most natural and ubiquitous way of representing geometry and data in 3D and higher. In this talk, I will present a framework of solving geometric PDEs directly on point clouds based on local tangent space parametrization. Then I will talk about some applications in shape analysis for point clouds. Unlike images, which have a canonical form of representation as functions defined on a uniform grid on a rectangular domain, surfaces and manifolds in 3D and higher are geometric objects that do not have a canonical or natural form of representation or global parametrization. Moreover, their embeddings in the ambient space are not intrinsic. We show how geometric PDEs can be used to “connect the dots” and extract intrinsic geometric information for the underlying point clouds for shape analysis.

12:30  2:30 pm EDTLunch/Free Time

2:30  3:15 pm EDTFinite element methods for illposed interface problems11th Floor Lecture Hall
 Speaker
 Erik Burman, University College London
 Session Chair
 Maxim Olshanskiy, University of Houston
Abstract
In this talk we will consider recent advances on the approximation of second order elliptic problems with interfaces that have poor, nonstandard stability, or are illposed. Such problems arise in a multitude of applications for example in seismic inversion problems or the design of meta materials. As a model problem we will consider the classical illposed problem of unique continuation in a heterogeneous environment. First we will discuss primaldual stabilized finite elements for the homogeneous case and recall recent results on the accuracy and optimality of such methods. Then we will show how the method can be modified to handle internal interfaces using an unfitted finite element method. We will report error estimates for this method and discuss how to handle the destabilizing effect of error in the geometrical data. Finally we will show how the ideas can be applied to socalled sign changing materials, where the coefficient of the diffusion operator is of different sign in different subdomain. The accurate approximation of wave propagation in such materials are important for the design of metamaterials.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space

4:00  4:45 pm EDTDivergence preserving cut finite element methods11th Floor Lecture Hall
 Speaker
 Sara Zahedi, KTH Royal Institute of Technology
 Session Chair
 Maxim Olshanskiy, University of Houston
Abstract
I will give an introduction to Cut Finite Element Methods (CutFEM) for interface problems and present our recent development that results in pointwise divergencefree velocity approximations of incompressible flows.
Wednesday, March 13, 2024

9:00  9:45 am EDTNavierStokes equations on surfaces: Analysis and numerical simulations11th Floor Lecture Hall
 Speaker
 Arnold Reusken, Aachen University
 Session Chair
 Ricardo Nochetto, University of Maryland
Abstract
In this presentation we consider a NavierStokes type system, posed on a smooth closed stationary or evolving twodimensional surface embedded in three dimensional space. We briefly address modeling aspects related to this system. We introduce the socalled tangential surface NavierStokes equations and discuss a wellposed weak variational formulation of this PDE system that forms the basis for finite element discretization methods. Furthermore we explain the basic ideas of an unfitted finite element method, known as TraceFEM, that is used in our numerical simulation of the tangential surface NavierStokes system. Results of numerical experiments with this method are presented that illustrate how lateral flows are induced by smooth deformations of a material surface.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTNodal FEM for the surface Stokes problem11th Floor Lecture Hall
 Speaker
 Alan Demlow, Texas A&M University
 Session Chair
 Ricardo Nochetto, University of Maryland
Abstract
The Stokes and NavierStokes problems formulated on surfaces present a number of challenges distinct from those encountered for the corresponding Euclidean equations. In the context of numerical methods, these include the inability to formulate standard surface finite element velocity fields which are simultaneously continuous (H1conforming) and tangential to the surface. In this talk we will give an overview of various finite element methods that have been derived for the surface Stokes problem, along with their advantages and drawbacks. We will then present a surface counterpart to the Euclidean MINI element which is the first FEM for the surface Stokes problem which does not require any penalization. Finally, we will briefly discuss extension to other nodal Stokes FEM such as TaylorHood elements. This is joint work with Michael Neilan.

11:30 am  12:15 pm EDTFluid flow on surfaces11th Floor Lecture Hall
 Speaker
 Gieri Simonett, Vanderbilt University
 Session Chair
 Ricardo Nochetto, University of Maryland
Abstract
I will consider the motion of an incompressible viscous fluid on compact manifolds (with or without boundary). Local in time wellposedness is established in the framework of $L_p$$L_q$ maximal regularity for initial values in critical spaces. It will be shown that the set of equilibria consists exactly of the Killing vector fields. Each equilibrium is stable and any solution starting close to an equilibrium converges at an exponential rate to a (possibly different) equilibrium. In case the surface is twodimensional, it will be shown that any solution with divergence free initial value in $L_2$ exists globally and converges to an equilibrium.

12:25  12:30 pm EDTGroup Photo (Immediately After Talk)11th Floor Lecture Hall

12:30  2:30 pm EDTMentoring Discussion for Early Career Researchers and Students (Organized by Susanne Brenner, Sara Pollock, Michael Neilan)Lunch/Free Time  11th Floor Collaborative Space

2:30  3:15 pm EDTTwophase flows on deformable surfaces11th Floor Lecture Hall
 Speaker
 Axel Voigt, Institute of Scientific Computing  Technische Universitat Dresden
 Session Chair
 Ricardo Nochetto, University of Maryland
Abstract
We extend the concept of fluid deformable surfaces to twophase flows. The equations are derived by a Larganged'Alembert principle and solved by surface finite elements. We demonstrate the huge possibilities of shape evolutions resulting from the strong interplay of phasedependent bending properties, the line tension and the surface viscosity.

3:30  5:00 pm EDT
Thursday, March 14, 2024

9:00  9:45 am EDTFinite Element Methods For Curvature11th Floor Lecture Hall
 Speaker
 Shawn Walker, Louisiana State University
 Session Chair
 Axel Voigt, Institute of Scientific Computing  Technische Universitat Dresden
Abstract
This talk presents some recent advances in extending the classic HellanHerrmannJohnson (HHJ) finite element to surfaces for approximation of bending problems and computing curvature. We give a review of the surface version of the HHJ method which leads to a convergent method to solve the surface Kirchhoff plate problem on surfaces embedded in three dimensions, along with numerical examples. We also describe a postprocessing technique for approximating the surface Hessian of a scalar function from discrete data. We show how this scheme is easily extended to give convergent approximations of the *full shape operator* of the underlying surface, even for piecewise linear triangulations. Several numerical examples are given on nontrivial surfaces to illustrate the method. We then show how the surface HHJ finite element can also be used in computing Willmore flow, which is a gradient flow for the bending energy. In particular, we present key identities for the derivation of the method and discuss its stability. Several numerical examples show the efficacy of the method.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTFinding equilibrium states of fluid membranes11th Floor Lecture Hall
 Speaker
 Maxim Olshanskiy, University of Houston
 Session Chair
 Axel Voigt, Institute of Scientific Computing  Technische Universitat Dresden
Abstract
We are interested in finding equilibrium configurations of inextensible elastic membranes exhibiting lateral fluidity. Differential equations governing the mechanical equilibrium are derived using a continuum description of the membrane motions given by the surface NavierStokes equations with bending forces. Equilibrium conditions that are found appear to be independent of lateral viscosity and relate tension, pressure and tangential velocity of the fluid. These conditions yield that only surfaces with Killing vector fields, such as axisymmetric shapes, can support nonzero stationary flow of mass. We derive a shape equation that extends a classical Helfrich model with area constraint to membranes of nonnegligible mass. We introduce a simple numerical method to compute solutions of this highly nonlinear equation. The numerical method is then applied to find a diverse family of equilibrium configurations.

11:30 am  12:15 pm EDTLiquid Crystal Variational Problems11th Floor Lecture Hall
 Speaker
 Ricardo Nochetto, University of Maryland
 Session Chair
 Axel Voigt, Institute of Scientific Computing  Technische Universitat Dresden
Abstract
We discuss modeling, numerical analysis and computation of liquid crystal networks (LCNs). These materials couple a nematic liquid crystal with a rubbery material. When actuated with heat or light, the interaction of the liquid crystal with the rubber creates complex shapes. Thin bodies of LCNs are natural candidates for soft robotics applications. We start from the classical 3D trace energy formula and derive a reduced 2D membrane energy as the formal asymptotic limit of vanishing thickness and characterize the zero energy deformations. We design a sound numerical method and prove its Gamma convergence despite the strong nonlinearity and lack of convexity properties of the membrane energy. We present computations showing the geometric effects that arise from liquid crystal defects as well as computations of nonisometric origami within and beyond the theory. This work is joint with L. Bouck and S. Yang.

12:30  2:30 pm EDTLunch/Free Time

2:30  3:15 pm EDTA finite element scheme for the Qtensor model of liquid crystals subjected to an electric field11th Floor Lecture Hall
 Virtual Speaker
 Franziska Weber, Carnegie Mellon University
 Session Chair
 Axel Voigt, Institute of Scientific Computing  Technische Universitat Dresden
Abstract
Liquid crystal is an intermediate state of matter between the liquid and the solid phase, where the elongated molecules of the material are in partial alignment. Due to this, materials exhibiting a liquid crystal phase have unique physical properties that are used in various reallife applications, such as monitors (LCDs), smart glasses, navigation systems, shampoos, and others. Various mathematical models are available to describe their dynamics, one commonly used one is the Qtensor model by Landau and de Gennes, in which the alignment of the liquid crystal molecules and its variation over time are described through systems of nonlinear PDEs. In this talk, I will describe this model for the case where the liquid crystal molecules are subject to an electric field and present an energystable numerical discretization for it. Furthermore, within a particular range of material parameters, the convergence of the scheme can be shown to a weak solution of the system of PDEs. This is a joint work with Max Hirsch (UC Berkeley).

3:30  4:00 pm EDT"Pi Day" Coffee BreakCoffee Break  11th Floor Collaborative Space

4:00  4:45 pm EDTNumerical Approximation of the Stochastic AllenCahn Equation11th Floor Lecture Hall
 Speaker
 Noel Walkington, Carnegie Mellon University
 Session Chair
 Axel Voigt, Institute of Scientific Computing  Technische Universitat Dresden
Abstract
Convergence theory for numerical approximations of the stochastic AllenCahn equation will be reviewed. This talk will illustrate how structural properties of the partial differential operator(s) and probabilistic methods can be combined to establish stability and convergence of numerical schemes to approximate martingale solutions of the AllenCahn equation. This is joint work with M. Ondrejat (Prague, CZ) and A. Prohl (Tuebingen, DE).
Friday, March 15, 2024

9:00  9:45 am EDTPDE: spectra, geometry and spectral geometry11th Floor Lecture Hall
 Speaker
 Nilima Nigam, Simon Fraser University
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
The spectra of elliptic operators are intricately connected to the geometrical properties of the spatial domains on which the operators are defined. Numerical computations are invaluable in studying this interplay, and highaccuracy discretizations are needed. This is particularly true of the Steklov problems. In this talk we'll present strategies for computing SteklovLaplace and SteklovHelmholtz spectra based on integral operators, and their efficacy in solving questions on the impact of geometry on spectral asymptotics. If time permits, we'll also present work in progress on a (modification of) the SteklovMaxwell problem.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTNumerical analysis of an evolving bulk–surface model of tumour growth11th Floor Lecture Hall
 Speaker
 Balázs Kovács, University of Paderborn
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
In this talk we will discuss an evolving bulksurface finite element method for a model of tissue growth, which is a modification of the model of Eyles, King and Styles (2019). The model couples a Poisson equation on the domain with a forced mean curvature flow of the free boundary, with nontrivial bulksurface coupling in both the velocity law of the evolving surface and the boundary condition of the Poisson equation. The numerical method discretizes evolution equations for the mean curvature and the outer normal and it uses a harmonic extension of the surface velocity into the bulk. The discretization admits a convergence analysis in the case of continuous finite elements of polynomial degree at least two. The stability of the discretized bulksurface coupling is a major concern. The error analysis combines stability estimates and consistency estimates to yield optimalorder H^1norm error bounds for the computed tissue pressure and for the surface position, velocity, normal vector and mean curvature. We will present some numerical experiments illustrating and complementing our theoretical results. The talk is based on joint work with D. Edelmann and Ch. Lubich (Tuebingen).

11:30 am  12:15 pm EDTPhase Field Models and Continuous Data Assimilation11th Floor Lecture Hall
 Speaker
 Amanda Diegel, Mississippi State University
 Session Chair
 Noel Walkington, Carnegie Mellon University
Abstract
Phase field models have become popular tools that help capture important features of a variety of physical processes. In this talk, we will briefly discuss two popular models for phase separation: the AllenCahn equation and the ChanHilliard equation. We will then discuss finite element methods that incorporate continuous data assimilation in order to achieve long time accuracy and stability for arbitrarily inaccurate initial conditions provided enough data measurements are incorporated into the simulation. We will demonstrate the effectiveness of our methods via several numerical experiments.

12:30  2:30 pm EDTLunch/Free Time

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Monday, March 18, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Tuesday, March 19, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTWaveHoltz: Parallel and Scalable Solution of the Helmholtz Equation via Wave Equation Iteration11th Floor Lecture Hall
 Daniel Appelö, Virginia Tech
Abstract
We introduce the WaveHoltz iteration, for solving the Helmholtz equation. The method is inspired by recent work on exact controllability (EC) methods and as in EC methods we make use of time domain methods for wave equations to design frequency domain Helmholtz solvers, but unlike EC methods we do not require adjoint solves. We show that the WaveHoltz iteration is symmetric and positive definite (compared to the indefinite Helmholtz equation). We present numerical examples, using various discretization techniques, that show that our method can be used to solve problems with rather high wave numbers.

12:00  12:30 pm EDTHybridizable discontinuous Galerkin methodPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Yukun Yue, University of Wisconsin, Madison

12:30  1:00 pm EDTPDE constrained optimizationPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Sijing Liu, Brown University

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Wednesday, March 20, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTBabuska's paradox in linear and nonlinear bending theories11th Floor Lecture Hall
 Soeren Bartels, University of Freiburg
Abstract
The plate bending or Babuska paradox refers to the failure of convergence when a linear bending problem with simple support boundary conditions is approximated using polygonal domain approximations. We provide an explanation based on a variational viewpoint and identify sufficient conditions that avoid the paradox and which show that boundary conditions have to be suitably modified. We show that the paradox also matters in nonlinear thinsheet folding problems and devise approximations that correctly converge to the original problem.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Thursday, March 21, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Friday, March 22, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Monday, March 25, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Tuesday, March 26, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTThe proximal Galerkin method11th Floor Lecture Hall
 Thomas Surowiec, SIMULA

12:00  1:00 pm EDTLong shortterm memory (LSTM) neural networksPost Doc/Graduate Student Seminar  11th Floor Conference Room
 John Carter, Rensselaer Polytechnic Institute

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Wednesday, March 27, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Thursday, March 28, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Friday, March 29, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:30  10:30 am EDTHiringProfessional Development  11th Floor Lecture Hall

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Monday, April 1, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Tuesday, April 2, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTA meeting of Stokes, Brinkman, and Darcy in polytopal multiscale hybrid methods11th Floor Lecture Hall
 Sônia Gomes, Universidade Estadual de Campinas

12:00  1:00 pm EDTRandomized SVDPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Christopher Wang, Cornell University

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Wednesday, April 3, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTNonoverlapping Spectral Additive Schwarz Methods11th Floor Lecture Hall
 Marcus Sarkis, Worcester Polytechnic Institute
Abstract
Nonoverlapping Spectral Additive Schwarz MethodsNOSAS is a family of adaptive Schwarz preconditioners designed originally for solving elliptic problems with highly heterogeneous coefficients with prescribed rate of convergence. And later extended to the Helmholtz problem. In this talk I will give a short introduction on Domain Decomposition methods as a preconditioner and then discuss NOSAS. This is a joint work with Yu Yi and Maksymilian Dryja.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Thursday, April 4, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Friday, April 5, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:30  10:30 am EDTPapersProfessional Development  11th Floor Lecture Hall

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Monday, April 8, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

2:00  3:00 pm EDTEclipse Coffee BreakCoffee Break  11th Floor Collaborative Space
Tuesday, April 9, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTAdaptive algorithms for stochastic finite element methods11th Floor Lecture Hall
 Alex Bespalov, University of Birmingham
Abstract
We will discuss a posteriori error estimation techniques and adaptive algorithms for two popular, albeit conceptually different, FEMbased solution strategies for partial differential equations with uncertain or parameterdependent inputs: one strategy is projectionbased (stochastic Galerkin FEM) and the other is samplingbased (stochastic collocation FEM).

12:00  1:00 pm EDTMultigrid Part 1Post Doc/Graduate Student Seminar  11th Floor Conference Room
 Sijing Liu, Brown University

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Wednesday, April 10, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:30  10:30 am EDTGrantsProfessional Development  11th Floor Lecture Hall

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Thursday, April 11, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTNumerical schemes for the CahnHilliard equation11th Floor Lecture Hall
 Giordano Tierra Chica, University of North Texas
Abstract
The study of interfacial dynamics has become a key component to understand the behavior of a great variety of systems, in scientific, engineering and industrial applications. A very effective approach for representing interface problems is the diffuse interface/phase field approach, which describes the interfaces by layers of small thickness and whose structure is determined by a balance of molecular forces, in such a way that the tendencies for mixing and demixing compete through a nonlocal mixing energy. This approach uses a phasefield function that takes distinct values in the pure phases (for instance 0 in one phase and 1 in the other one) and varies smoothly in the interfacial regions. In particular, the Cahn Hilliard equation was originally introduced to model the thermodynamic forces driving phase separation, arriving to a system with a gradient flow structure, that is, when there are no external forces applied to the system, the total free energy of the mixture is not increasing in time. In the first part of the presentation I will talk about the CahnHilliard model and the main ideas behind the derivation of numerical schemes, showing the main advantages and disadvantages of each approach. The key point is to try to preserve the properties of the original model while the numerical schemes are efficient in time. On the second part I will present two new numerical schemes to approximate the CahnHilliard equation with degenerate mobility. I will show that both schemes are energy stable and boundedness preserving, in the sense that the amount of the solution being outside of the interval [0, 1] goes to zero in terms of a truncation parameter. Finally, I will discuss how the ideas considered for designing numerical schemes to approximate phasefields models can be extended to other energy based applications, like liquid crystals or mixture of fluids.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Friday, April 12, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

9:00  10:00 am EDTFinite element discretization of nonlocal problemsTutorial  11th Floor Lecture Hall
 Christian Glusa, Sandia National Lab

10:00  10:30 am EDTBreakCoffee Break

10:30 am  12:30 pm EDTHandson session for PyNucleusTutorial  11th Floor Lecture Hall
 Christian Glusa, Sandia National Lab
Abstract
Instructions for installing and running "PyNucleus" can be found here: https://sandialabs.github.io/PyNucleus/installation.html In order to make sure that learners can run the code locally on their machine on the day of the tutorial, I would recommend to follow the instructions for using the container image beforehand.

12:30  1:30 pm EDTLunch/Free Time

1:30  2:30 pm EDTOverview of peridynamics and its meshfree discretizationTutorial  11th Floor Lecture Hall
 Pablo Seleson, Oak Ridge National Laboratory (ORNL)
Abstract
Peridynamics is a popular nonlocal formulation of continuum mechanics which has been shown to be highly effective for fracture modeling. A meshfree discretization is commonly used in peridynamics for engineering computations due to its implementation simplicity and its ability to easily handle large deformation and material separation. The PDMATLAB2D code is a meshfree peridynamics implementation in MATLAB suitable for simulation of twodimensional fracture problems. PDMATLAB2D provides an entrylevel peridynamics computational tool for both research and education. This short course will provide an overview of peridynamics and its meshfree discretization followed by a description of the PDMATLAB2D code and a handson session with illustrative examples.
Materials: PDMATLAB2D code: The code can be downloaded from GitHub: https://github.com/ORNL/PDMATLAB2D/ PDMATLAB2D paper: The following paper describes the code: P. Seleson, M. Pasetto, Y. John, J. Trageser, S. T. Reeve. PDMATLAB2D: A Peridynamics MATLAB Twodimensional Code. J Peridyn Nonlocal Model (2024). https://doi.org/10.1007/s4210202300104w
The paper can be accessed by clicking here.
Instructions:
Although it is not required, it is recommended that shortcourse attendees read the paper and download and run the code before the short course. The code can be directly downloaded by selecting the green Code button on the main repository page. Examples can be run (from the toplevel directory) in MATLAB using either of the following commands: PDMATLAB2D('WavePropagation') PDMATLAB2D('CrackBranching') 
2:30  3:00 pm EDTCoffee Break11th Floor Collaborative Space

3:00  5:00 pm EDTDescription of PDMATLAB2D and handson sessionTutorial  11th Floor Lecture Hall
 Pablo Seleson, Oak Ridge National Laboratory (ORNL)
Abstract
PDMATLAB2D: A Meshfree Peridynamics MATLAB Code for 2D Fracture Computations
Peridynamics is a popular nonlocal formulation of continuum mechanics which has been shown to be highly effective for fracture modeling. A meshfree discretization is commonly used in peridynamics for engineering computations due to its implementation simplicity and its ability to easily handle large deformation and material separation. The PDMATLAB2D code is a meshfree peridynamics implementation in MATLAB suitable for simulation of twodimensional fracture problems. PDMATLAB2D provides an entrylevel peridynamics computational tool for both research and education. This short course will provide an overview of peridynamics and its meshfree discretization followed by a description of the PDMATLAB2D code and a handson session with illustrative examples.
Materials: PDMATLAB2D code: The code can be downloaded from GitHub: https://github.com/ORNL/PDMATLAB2D/ PDMATLAB2D paper: The following paper describes the code: P. Seleson, M. Pasetto, Y. John, J. Trageser, S. T. Reeve. PDMATLAB2D: A Peridynamics MATLAB Twodimensional Code. J Peridyn Nonlocal Model (2024). https://doi.org/10.1007/s4210202300104w <br< The paper can be accessed by clicking here. Instructions: Although it is not required, it is recommended that shortcourse attendees read the paper and download and run the code before the short course. The code can be directly downloaded by selecting the green Code button on the main repository page. Examples can be run (from the toplevel directory) in MATLAB using either of the following commands: PDMATLAB2D('WavePropagation') PDMATLAB2D('CrackBranching')
Monday, April 15, 2024

8:30  8:50 am EDTCheck In11th Floor Collaborative Space

8:50  9:00 am EDTWelcome11th Floor Lecture Hall

9:00  10:20 am EDTAn invitation to nonlocal modelsTutorial  11th Floor Lecture Hall
 Speaker
 Xiaochuan Tian, University of CaliforniaSan Diego
 Session Chair
 Abner Salgado, University of Tennessee
Abstract
There has been a growing interest in studying nonlocal models as more general and sometimes more realistic alternatives to conventional PDE models. In this tutorial, we will introduce nonlocal models. In particular, we will focus on the nonlocal models with a finite range of nonlocal interactions, which connect the classical PDEs, nonlocal discrete models, and fractional differential equations. This talk will cover nonlocal modeling, nonlocal vector calculus, and numerical analysis for the nonlocal models.

10:30  11:00 am EDTCoffee Break11th Floor Collaborative Space

11:00 am  12:20 pm EDTTutorial on fractional calculusTutorial  11th Floor Lecture Hall
 Speaker
 Abner Salgado, University of Tennessee
 Session Chair
 Pablo Seleson, Oak Ridge National Laboratory (ORNL)
Abstract
We will discuss several approaches to extend the notion of a derivative to a fractional order, their meaning, limitations, and possible applications. For some of these, we will study the existence, uniqueness, and regularity of solutions to initial boundary value problems with these operators, and their implications for numerical approximations

12:30  2:00 pm EDTLunch/Free Time

2:00  3:20 pm EDTTutorial on peridynamicsTutorial  11th Floor Lecture Hall
 Speaker
 Pablo Seleson, Oak Ridge National Laboratory (ORNL)
 Session Chair
 Xiaochuan Tian, University of CaliforniaSan Diego
Abstract
Peridynamics is a powerful nonlocal reformulation of classical continuum mechanics, suitable for material failure and damage simulation, which has become very popular in recent years. In contrast to classical constitutive relations that employ spatial differential operators, peridynamic models use spatial integral operators which do not require spatial differentiability assumptions of displacement fields. This enables a natural representation of material discontinuities such as cracks. Peridynamic models possess length scales, making them also suitable for multiscale modeling. This tutorial will provide an overview of peridynamics, including some of its mathematical, computational, and modeling aspects.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space

4:00  4:45 pm EDTAnalytical and applied aspects for nonlocal curvature11th Floor Lecture Hall
 Speaker
 Petronela Radu, University of Nebraska, Lincoln
 Session Chair
 Xiaochuan Tian, University of CaliforniaSan Diego
Abstract
Curvature is a fundamental concept in physics and it plays a crucial role in various areas such as: classical mechanics, general relativity, optics, and fluid dynamics. In particular, the curvature of surfaces can affect the mechanical, electrical, and optical properties of materials, so curvature effects need to be taken into account when designing and analyzing new materials. The recently introduced concept of nonlocal curvatures provide a frameworks for measuring the “bend” of a surface under little or no smoothness assumptions, while connecting to classical curvature as the horizon of interaction converges to zero. In this talk I will focus on two distinct problems: of constant nonlocal curvature and of ordered curvature and show how they relate to their classical counterparts.

5:00  6:30 pm EDTReception11th Floor Collaborative Space
Tuesday, April 16, 2024

9:00  9:45 am EDTQuasilinear fractional operators in Lipschitz domains: regularity and approximation11th Floor Lecture Hall
 Speaker
 Ricardo Nochetto, University of Maryland
 Session Chair
 Abner Salgado, University of Tennessee
Abstract
Fractional diffusion in bounded domains is notorious for the lack of boundary regularity of solutions regardless of the smoothness of domain boundary. We explore this matter for the homogeneous Dirichlet problem for fractionalorder quasilinear operators with variable coefficients in Lipschitz domains and any dimensions; this includes fractional pLaplacians and operators with finite horizon. We prove lift theorems in Besov norms which are consistent with the boundary behavior of solutions in smooth domains. The proof exploits the underlying variational structure and uses a new and flexible local translation operator. We further apply these regularity estimates to derive novel error estimates for finite element approximations of fractional pLaplacians and present several simulations that reveal the boundary behavior of solutions.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTThe role of fractional diffusion in the optimal recovery of partial differential equations without boundary conditions11th Floor Lecture Hall
 Speaker
 Andrea Bonito, Texas A&M University
 Session Chair
 Abner Salgado, University of Tennessee
Abstract
The problem of learning an unknown function from given data, i.e., construct an approximation to the function that predicts its values away from the data is ubiquitous in modern science. There are numerous settings for this learning problem depending on what additional information is provided about the unknown function, how the accuracy is measured, what is known about the data and data sites, and whether the data observations are polluted by noise. We consider the specific case where the learning problem consists of determining the solution to a second order elliptic partial differential equation (PDE) without information on its boundary condition. The lack of sufficient information necessary to uniquely determine the targeted function is alleviated by given finitely many linear noiseless measurements. The recovery performance is measured in energy norm and for the recovery problem to be tractable, we assume that the function to be recovered belongs to a compact subset of the energy space. The latter is referred to as the model class assumption. Among all functions satisfying the measurements, the PDE, and the model class assumption, the proposed algorithm constructs an approximation of the representative with minimal norm. For each measurement, this requires the approximation of a fractional diffusion problem on the boundary of the computational domain. We present and discuss the performances of the DunfordTaylor method employed for the space discretization. In addition, we show how the resulting recovery algorithm is nearoptimal and asymptotically optimal in the limit of the vanishing space discretization error.

11:30 am  12:15 pm EDTPeridynamic differential operator for optimum 3D Point Cloud Data manipulation11th Floor Lecture Hall
 Speaker
 Erdogan Madenci, University of Arizona
 Session Chair
 Abner Salgado, University of Tennessee
Abstract
Point cloud data (PCD) represents 3D spatial information as a set of points in a 3D coordinate system, typically obtained from various sensors such as LiDAR (Light Detection And Ranging). 3D PCD generated by LiDAR is accurate and precise; however, it can be massive, requiring significant storage and computational resources for extracting meaningful information from PCD sets. Also, the sparse and irregular nature of the data renders the compression process a challenging task. Furthermore, the raw data inevitably may contain outliers or noise in realworld situations. Majority of the current methods for compression of 3D PCD usually use sampling approaches to select points from the original point clouds for conducting local feature learning. A major drawback of the existing methods is that they are highly dataspecific, challengespecific, or approachspecific. This study presents an approach for optimized manipulation of 3D PCD to achieve high fidelity reconstruction of 3D LiDAR data by employing the Peridynamic Differential Operator (PDDO). It is a single mathematical framework for optimum and accurate data compression and decompression in the presence of irregularities and scatter in a multidimensional space. The capability of this approach is demonstrated by considering a 3D LiDAR PCD for adaptive data compression and recovery.

12:30  2:30 pm EDT

2:30  3:15 pm EDTCoupled local and nonlocal models11th Floor Lecture Hall
 Speaker
 Juan Pablo Borthagaray, Universidad de la República, Uruguay
 Session Chair
 Abner Salgado, University of Tennessee
Abstract
We consider theoretical and computational aspects of models coupling local and nonlocal operators with variable diffusivity. We discuss the wellposedness and strong formulation of such problems, as well as the regularity of weak solutions. We also focus on the approximation of solutions by a conforming finite element method.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space

4:00  4:45 pm EDTScalable methods for nonlocal models11th Floor Lecture Hall
 Speaker
 Christian Glusa, Sandia National Lab
 Session Chair
 Abner Salgado, University of Tennessee
Abstract
The naive discretization of nonlocal operators leads to matrices with significant density, as compared to classical PDEs. This makes the efficient solution of nonlocal models a challenging task. In this presentation, we will discuss ongoing research into efficient hierarchical matrix assembly and geometric and algebraic multigrid preconditioners that are suitable for nonlocal models.
Wednesday, April 17, 2024

9:00  9:45 am EDTSome challenges in the numerical simulation of nonlocal models with a finite range of interactions.11th Floor Lecture Hall
 Speaker
 Qiang Du, Columbia University
 Session Chair
 Xiaochuan Tian, University of CaliforniaSan Diego
Abstract
Nonlocal models associated with a finite horizon parameter that characterizes the effective range of nonlocal interactions have demonstrated potential as effective alternatives to local partial differential equations (PDEs) in various applications, such as the study of fracture and damage using peridynamics, and traffic flow of autonomous vehicles using nonlocal traffic models. However, to enhance the predictive capabilities and broaden the applicability of such models, new challenges arise in their numerical simulations. We provide illustrative examples, encompassing both theoretical analysis and practical implementation. These include choices regarding discretization methods, approximations of interaction neighborhoods, treatment of interfaces and boundaries, learning of nonlocal interactions from data, a priori and a posteriori error estimations, adaptive computation techniques, and the development of fast and scalable solvers.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTThe role of boundary constraints in simulating biological systems with nonlocal dispersal.11th Floor Lecture Hall
 Speaker
 Gabriela Jaramillo, University of Houston
 Session Chair
 Xiaochuan Tian, University of CaliforniaSan Diego
Abstract
Population and vegetation models often use nonlocal forms of dispersal to describe the spread of individuals and plants. When these longrange effects are modeled by spatially extended convolution kernels, the mathematical analysis of solutions can be simplified by posing the relevant equations on unbounded domains. However, in order to numerically validate these results, these same equations then need to be restricted to bounded sets. Thus, it becomes important to understand what effects, if any, do the different boundary constraints have on the solution. To address this question we present a quadrature method valid for convolution kernels with finite second moments. This scheme is designed to approximate at the same time the convolution operator together with the prescribed nonlocal boundary constraints, which can be Dirichlet, Neumann, or what we refer to as free boundary constraints. We then apply this scheme to study pulse solutions of an abstract 1d nonlocal GrayScott model as a case study. We consider convolution kernels with exponential and with algebraic decay. We find that, surprisingly, boundary effects can be more prominent when using exponentially decaying kernels.

11:30 am  12:15 pm EDTPointwiseintime apriori and aposteriori error control for timefractional subdiffusion equations11th Floor Lecture Hall
 Speaker
 Natalia Kopteva, University of Limerick
 Session Chair
 Xiaochuan Tian, University of CaliforniaSan Diego
Abstract
Over the past decade, there has been a growing interest in evolution equations of parabolic type that involve fractionalorder derivatives in time of order in (0, 1). Such equations, also called subdiffusion equations, arise in various applications in engineering, physics, biology and finance. Hence, it is quite important to develop efficient and reliable computational tools for their numerical solution. For such problems, I will present a simple and general numericalstability analysis using barrier functions, which yields sharp pointwiseintime error bounds. Furthermore, pointwiseintime a posteriori error bounds will be given, which lead to an adaptive time stepping algorithm with a local time step criterion.

12:25  12:30 pm EDTGroup Photo (Immediately After Talk)11th Floor Lecture Hall

12:30  12:40 pm EDTSemester Program group photosGroup Photo (Immediately After Talk)  11th Floor Lecture Hall

12:40  2:30 pm EDTLunch/Free Time

2:30  3:15 pm EDTLearning Nonlocal Operators for Material Modeling11th Floor Lecture Hall
 Speaker
 Yue Yu, Lehigh University
 Session Chair
 Xiaochuan Tian, University of CaliforniaSan Diego
Abstract
During the last 20 years there has been a lot of progress in applying neural networks (NNs) to many machine learning tasks. However, their employment in scientific machine learning with the purpose of learning complex responses of physical systems from experimental measurements has been explored much less. Unlike classical machine learning tasks, such as computer vision and natural language processing where a large amount of unstructured data are available, physicsbased machine learning tasks often feature scarce and structured measurements. In this talk, we will consider learning of heterogeneous material responses as an exemplar problem to investigate automated physical model discovery from experimental data. In particular, we propose to parameterize the mapping between excitation and corresponding system responses in the form of nonlocal neural operators, and infer the neural network parameters from experimental measurements. As such, the model is built as mappings between infinitedimensional function spaces, and the learnt network parameters are resolutionagnostic: measurements with different resolutions can be integrated to train the same model. Moreover, the nonlocal operator architecture also allows the incorporation of fundamental mathematical and physics knowledge, which further improves the learning efficacy and robustness from scarce measurements. To demonstrate the applicability of our nonlocal operator learning framework, three typical scenarios in physicsbased machine learning will be discussed: (1) learning of a materialspecific constitutive law, (2) learning of an efficient PDE solution operator, and (3) development of a foundation constitutive law across multiple materials. As an application, we learn material models directly from digital image correlation (DIC) displacement tracking measurements on a porcine tricuspid valve leaflet tissue, and we will show that the learnt model substantially outperforms conventional constitutive models.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space

4:00  5:00 pm EDTTOPIC: Where is "nonlocal" going?Panel Discussion  11th Floor Lecture Hall
 Moderator
 Abner Salgado, University of Tennessee
 Panelists
 Florin Bobaru, UNIVERSITY OF NEBRASKA–LINCOLN
 Qiang Du, Columbia University
 Ricardo Nochetto, University of Maryland
Thursday, April 18, 2024

9:00  9:45 am EDTA FeynmanKac probabilistic approach for the computation of nonlocal transport11th Floor Lecture Hall
 Speaker
 Diego del CastilloNegrete, ORNL
 Session Chair
 Juan Pablo Borthagaray, Universidad de la República, Uruguay
Abstract
A novel method to compute nonlocal transport is presented [1]. The models of interest are FokkerPlanck type equations in which local (e.g., diffusive) transport is represented by differential operators and nonlocal transport by integrodifferential operators (e.g., nondiffusive turbulent transport closures and fractional diffusion). Computational approaches for these problems can be roughly classified as deterministic methods (e.g., finitedifference) and stochastic methods (e.g., MonteCarlo). Although extensively used, continuum deterministic methods face stability and scalability challenges specially in the case of nonlocal operators that result in dense (nonsparse) matrices. On the other hand, particlebased stochastic methods face poor convergence due to statistical sampling. Here we present an alternative approach based on the FeynmanKac theory that establishes a link between the FokkerPlanck equation and the stochastic differential equation of the underlying stochastic process. In local transport, the SDE are driven by Brownian motion and in nonlocal transport, depending on the nonlocal kernel, by compound Poisson processes or alphastable processes. The proposed method reduces the computation to the evaluation of expectations (bypassing the need of sampling stochastic trajectories) and it is unconditionally stable and parallelizable. We present applications to exit time and initial value problems for local and nonlocal transport in fluid dynamics and plasma physics. [1] M. Yang, G. Zhang, D. delCastilloNegrete, and Y.Cao, “A probabilistic scheme for semilinear nonlocal diffusion equations with volume constrains.” SIAM Journal of Numerical Analysis 61, (6), 27182743 (2023).

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTThe "flexibility" of the peridynamic horizon11th Floor Lecture Hall
 Speaker
 Florin Bobaru, UNIVERSITY OF NEBRASKA–LINCOLN
 Session Chair
 Juan Pablo Borthagaray, Universidad de la República, Uruguay
Abstract
In any nonlocal model of a physical phenomenon, one has to decide the extent of the nonlocality. In peridynamics (PD), nonlocality is defined by the “horizon region”, with its radius (when taken as a circular disk in 2D, or a sphere in 3D), the PD horizon size. In certain cases, particular material length scales are prominent and measurable. For example, the size of inclusions in a matrix leads to specific wave scattering effects. Material properties (elastic moduli, fracture toughness) and loading conditions also lead to identifiable length scales. One can then select the PD horizon (and the PD kernel) of a homogenized model of heterogeneous material to be in the same range as that particular length scale. A more complex scenario is when several length scales are present in a material, and not all of them are easily quantifiable, or when they combine in some complex fashion. I will discuss the options for selecting a “proper” size for the peridynamic horizon for a particular model. While for generic isotropic and homogeneous elastic materials subject to homogeneous deformations one can select an arbitrary horizon size (the response will be the same, in the bulk), when deformations are not homogeneous, as they happen to be near holes or notches, the horizon has to be selected in relation to the dimensions of these geometrical details. When a material has such a multitude of holes that we have to “rename” it as a porous material, homogenization can be used to allow using a much larger horizon size than the minute size of the pores. In fracture problems, regular homogenization for such materials may not be able to capture the observed failure modes, and an “intermediatelyhomogenized” PD model may be needed to be able to predict fracture and damage in such material systems at a lower cost than models that represent the detailed, porelevel microstructure. Distinguishing experiments that create thresholds in terms of crack behavior can also be used to decide on the appropriate size of the peridynamic horizon. An example from thermallyinduced fracture in glass will be shown. I will conclude with progress in speeding up peridynamic computations that use the fast convolutionbased method (FCBM, see PeriFast codes on GitHub).

11:30 am  12:15 pm EDTEfficient approximation of nonlocal phasefield models in the context of solidification11th Floor Lecture Hall
 Speaker
 Olena Burkovska, Oak Ridge National Laboratory
 Session Chair
 Andrea Bonito, Texas A&M University
Abstract
Phasefield modeling is widely used in material science to describe dynamics of substances with multiple phases, and it is indispensable to model solidification of materials in additive manufacturing. The interface between two substances, which is usually sharp, is replaced by a diffuse interface phasefield model that avoids an explicit tracking of the moving boundary. Resolving very thin interfaces in existing phasefield models requires very fine meshes or adding more structural complexity to the model in order to accurately capture the sharp interface. As a remedy, we propose a novel nonisothermal model with coupled localnonlocal dynamics. This provably allows to provide sharper interfaces in the solution up to the mesh resolution and can help to alleviate the limitations of resolving thin diffuse interfaces. Additionally, we design spatial and temporal discretization schemes that allow for efficient practical realizations of those models. We propose first and secondorder timestepping schemes, which guarantee energy stability in the isothermal case, and combine them with the finite element or spectral approximations in space. A particular structure of the model and nonlocal kernel allows for the characterization of the solution in terms of a projection formula. This provides an efficient way to evaluate the solution, and in some cases avoids solving a coupled nonlinear and nonlocal system.

12:30  2:30 pm EDTNetworking LunchLunch/Free Time  11th Floor Collaborative Space

2:30  3:15 pm EDTOn overcoming challenges in capturing corrosion dynamics using a nonlocally defined degradation field11th Floor Lecture Hall
 Speaker
 Cynthia Flores, California State University, Channel Islands
 Session Chair
 Andrea Bonito, Texas A&M University
Abstract
In this talk, we introduce corrosion degradation fields defined nonlocally. Emphasizing the challenge of defining boundary conditions for the nonlocal generation of current density maps, we explore the application of a simplified nonlocal Finite Element (FE) fractionalLaplacian solver for modeling corrosion degradation, with a particular focus on aerospace environments. We discuss the involvement of undergraduate researchers and the interdisciplinary validation of theoretical models through experimental data obtained using a Scanning Reference Electrode Technique (SRET) apparatus, with a specific focus on the aerospace context and the consideration of Microbiologically Induced Corrosion (MIC).

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space

4:00  5:00 pm EDTTOPIC: Funding opportunitiesPanel Discussion  11th Floor Lecture Hall
 Moderator
 Yue Yu, Lehigh University
 Panelists
 Yuliya Gorb, NSF
 Michael Parks, Oak Ridge National Laboratory
 Ludmil Zikatanov, The Pennsylvania State University
Friday, April 19, 2024

9:00  9:45 am EDTComputation and applications of the variableorder fractional Laplacian11th Floor Lecture Hall
 Speaker
 Yanzhi Zhang, Missouri University of Sciences and Technology
 Session Chair
 Xiaochuan Tian, University of CaliforniaSan Diego
Abstract
The variableorder fractional Laplacian plays an important role in the study of heterogeneous systems. However, current numerical methods for computing the variableorder fractional Laplacian still remain limited. Compared to its constantorder counterpart, the combination of nonlocality and heterogeneity in variableorder fractional Laplacian introduces significant storage and computational challenges. Consequently, many numerical methods developed for the constantorder fractional Laplacian become ineffective for computing the variableorder cases. In this talk, we introduce two numerical methods for the the variableorder Laplacian as well as their fast implementation. Some application of the variableorder fractional Laplacian in diffusion and wave propagation in heterogeneous media will also be discussed.

10:00  10:30 am EDTCoffee Break11th Floor Collaborative Space

10:30  11:15 am EDTAnalysis of Crack Patterns and Multiterm Timefractional Dynamics using CNNs and Fractional Physicsinformed DeepONet11th Floor Lecture Hall
 Speaker
 Guang Lin, Purdue University
 Session Chair
 Christian Glusa, Sandia National Lab
Abstract
In this talk, we first introduce convolutional neural networks designed to predict and analyze damage patterns on a disk resulting from molecular dynamic collision simulations. We propose the use of neural network approximations to complement peridynamic simulations by providing quick estimates which maintain much of the accuracy of the full simulations while reducing simulation times by a factor of 1500. We propose two distinct convolutional neural networks: one trained to perform the forward problem of predicting the damage pattern on a disk provided the location of a colliding object’s impact, and another trained to solve the inverse problem of identifying the collision location, angle, velocity, and size given the resulting damage pattern. Second, we introduce a Physicsinformed DeepONet model to solve the multiterm timefractional mixed diffusion equations. Deep Operator networks such as DeepONet can approximate nonlinear operators between infinite dimensional Banach spaces. PINNDeepONet employs deep neural operator networks with the PDEs explicitly encoded into the Neural Networks using automatic differentiation to impose the underlying physical laws via soft penalty constraints during model training. Here we extend PINNDeepONet to fractional physicsinformed DeepONets to solve multiterm timefractional mixed diffusionwave equations. We demonstrate that the proposed method can not only yield a significant improvement in predictive accuracy but also reduce the need for large training data sets.

11:30 am  12:15 pm EDTAnalysing the temporal accuracy of a hierarchy of coarsegrained SDE models11th Floor Lecture Hall
 Speaker
 Xingjie Li, University of North Carolina at Charlotte
 Session Chair
 Abner Salgado, University of Tennessee
Abstract
Coarsegraining or model reduction of dynamical systems is a modelling tool used to extend the timescale of simulations in a range of fields. When applied in molecular dynamics with moderate timescale separations, standard coarsegraining approaches seek to approximate the potential of mean force, and use this to drive an effective model. Meanwhile, there is no free lunch: fewer degrees of freedom necessarily means lower accuracy of the resulting model. Here, I will discuss work with Dr Thomas Hudson from the University of Warwick, UK, in which we derived a hierarchy of models to coarsegrain simple systems and analysed the resulting models. It is shown that while the standard recipe for model reduction accurately captures equilibrium statistics, it is possible to derive an easytoimplement Markovian effective model to better capture dynamical statistics such as the meansquared displacement. Our results focus particularly on the temporal accuracy of coarsegrained models. Both analytical and numerical evidence for the efficacy of the new approach is provided.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Monday, April 22, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Tuesday, April 23, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  1:00 pm EDTMultigrid Part 2Post Doc/Graduate Student Seminar  11th Floor Conference Room
 Casey Cavanaugh, Louisiana State University

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Wednesday, April 24, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTTaylorSeries Expansion for Meshfree Methods in Computational Solid Mechanics11th Floor Lecture Hall
 Yuri Bazilevs, Brown University
Abstract
Meshfree methods, such as the Reproducing Kernel Particle Method (RKPM) are entering their fourth decade of development. While a powerful concept, the ability of meshfree methods like RKPM to realize their full potential hinges on overcoming several technological challenges. One of them, and perhaps the thorniest of all, is the development of numerical integration or quadrature techniques that are stable, efficient, and accurate. In FEM, or in IGA, the decomposition of the problem domain into elements allows the analyst to develop quadrature techniques that are elementbased. Because approximation functions are infinitely smooth over the element interiors, Gaussian quadrature presents a provably accurate solution that hinges on its ability to optimally integrate smooth functions. However, the meshfree nature of methods like RKPM makes the definition of integration zones somewhat ambiguous. In addition, the infinite smoothness property inside the integration zones is also lost. As a result, traditional quadrature methods do not present a good practical solution for RKPM and similar techniques.
Numerical quadrature based on Taylorseries expansion approaches was introduced in the mid80s for FEM to develop a parameterfree approach for hourglass control. It laid dormant for a while, and, much later, in the mid 2010s, it resurfaced in the context of RKPM to develop a socalled Natural Stabilization approach, which is arguably the most important recent breakthrough in RKPM that brought the necessary added robustness for a wide range of nonlinear solid mechanics applications. In this talk, I will present a general framework of Taylorexpansionbased methods in computational solid mechanics and its broad applicability to meshfree methods and beyond. I will demonstrate: i. How to develop Taylorseriesexpansionbased formulations that are accurate and stable for nearly incompressible deformations; ii. How to stabilize correspondencebased Peridynamics without resorting to costly bondassociated approaches; and iii. How to develop generalpurpose largedeformation meshfree thin shells. 
3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Thursday, April 25, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

11:00 am  12:00 pm EDTA quasiincompressible ChanHilliardDarcy model for twophase flows in porous media11th Floor Lecture Hall
 Daozhi Han, State University of New York at Buffalo
Abstract
Twophase flows in porous media is known as the Muskat problem. The Muskat problem can be illposed. In this talk we introduce a quasiincompressible CahnHilliardDarcy model as a relaxation of the Muskat problem. We show global existence of weak solution to the model. We then present a high order accurate boundpreserving and unconditionally stable numerical method for solving the equations.

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Friday, April 26, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Monday, April 29, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Tuesday, April 30, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  1:00 pm EDTThe unfitted finite element methodPost Doc/Graduate Student Seminar  11th Floor Conference Room
 Henry von Wahl, Friedrich Schiller University Jena

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Wednesday, May 1, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

12:00  1:00 pm EDTEnd of Semester LunchWorking Lunch  11th Floor Collaborative Space

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Thursday, May 2, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
Friday, May 3, 2024
Numerical PDEs: Analysis, Algorithms, and Data Challenges

3:30  4:00 pm EDTCoffee Break11th Floor Collaborative Space
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