Confirmed Speakers & Participants

Workshop Schedule

Poster Session Gallery

Learning block-oriented nonlinear models of dynamical systems from data

Victor Gosea

Gradient-Based Recovery of Linear Projections in Multi-Index Models

Patrick Martin

System-Theoretic Model Reduction in pyMOR

Petar Mlinarić

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Elizabeth Qian

Data-Driven Discovery of Emergent Behaviors

Ming Zhong

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ORCID iD

As this program is funded by the National Science Foundation (NSF), ICERM is required to collect your ORCID iD if you are receiving funding to attend this program. Be sure to add your ORCID iD to your Cube profile as soon as possible to avoid delaying your reimbursement.

Acceptable Costs

• 1 roundtrip between your home institute and ICERM
• Flights on U.S. or E.U. airlines – economy class to either Providence airport (PVD) or Boston airport (BOS)
• Ground Transportation to and from airports and ICERM.

Unacceptable Costs

• Flights on non-U.S. or non-E.U. airlines
• Flights on U.K. airlines
• Seats in economy plus, business class, or first class
• Change ticket fees of any kind
• Multi-use bus passes
• Meals or incidentals

Advance Approval Required

• Personal car travel to ICERM from outside New England
• Multiple-destination plane ticket; does not include layovers to reach ICERM
• Arriving or departing from ICERM more than a day before or day after the program
• Multiple trips to ICERM
• Rental car to/from ICERM
• Flights on a Swiss, Japanese, or Australian airlines
• Arriving or departing from airport other than PVD/BOS or home institution's local airport
• 2 one-way plane tickets to create a roundtrip (often purchased from Expedia, Orbitz, etc.)

Reimbursement Request Form

https://icerm.brown.edu/money/

Refer to the back of your ID badge for more information. Checklists are available at the front desk.

Reimbursement Tips

• Scanned original receipts are required for all expenses
• Airfare receipt must show full itinerary and payment
• ICERM does not offer per diem or meal reimbursement
• Allowable mileage is reimbursed at prevailing IRS Business Rate and trip documented via pdf of Google Maps result
• Keep all documentation until you receive your reimbursement!

Reimbursement Timing

6 - 8 weeks after all documentation is sent to ICERM. All reimbursement requests are reviewed by numerous central offices at Brown who may request additional documentation.

Reimbursement Deadline

Submissions must be received within 30 days of ICERM departure to avoid applicable taxes. Submissions after thirty days will incur applicable taxes. No submissions are accepted more than six months after the program end.

Associated Semester Workshops

Model and dimension reduction in uncertain and dynamic systems

Jan 27 - May 1, 2020

Semester Program

Model and dimension reduction in uncertain and dynamic systems
Jan 27 - May 1, 2020

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Today's computational and experimental paradigms feature complex models along with disparate and, frequently, enormous data sets. This necessitates the development of theoretical and computational strategies for efficient and robust numerical algorithms that effectively resolve the important features and characteristics of these complex computational models. The desiderata for resolving the underlying model features is often application-specific and combines mathematical tasks like approximation, prediction, calibration, design, and optimization. Running simulations that fully account for the variability of the complexities of modern scientific models can be infeasible due to the curse of dimensionality, chaotic behavior or dynamics, and/or overwhelming streams of informative data.

This semester program focuses on both theoretical investigation and practical algorithm development for reduction in the complexity - the dimension, the degrees of freedom, the data - arising in these... (more)

Organizing Committee

• Yanlai Chen
• Serkan Gugercin
• Misha Kilmer
• Yvon Maday
• Shari Moskow
• Akil Narayan
• Daniele Venturi

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Mathematics of Reduced Order Models

Feb 17 - 21, 2020

Semester Workshop

Mathematics of Reduced Order Models
Feb 17 - 21, 2020

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Mathematical models of scientific applications often involve simulations with a large number of degrees of freedom that strain even the most efficient of algorithms. A clear need is the rigorous development of models with reduced complexity that retain fidelity to the application. Mathematics-based reduced-order modeling applies techniques in nonlinear approximation, projection-based discretizations, sparse surrogate construction, and high-dimensional approximation, in order to construct a model surrogate with near-optimal approximation properties. This workshop focuses on theoretical and algorithmic advances in mathematics-based model order reduction of various types: reduced basis methods, projection-based methods for dynamical systems, and sparse and low-rank approximations in high dimensions.

Organizing Committee

• Peter Benner
• Albert Cohen
• Serkan Gugercin
• Olga Mula
• Akil Narayan
• Karen Veroy-Grepl

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WORKSHOP ONLY OFFERED VIRTUALLY: Computational Statistics and Data-Driven...

Apr 20 - 24, 2020

Semester Workshop

WORKSHOP ONLY OFFERED VIRTUALLY: Computational Statistics and Data-Driven Models
Apr 20 - 24, 2020

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The advancement in computing and storage capabilities of modern computational clusters fosters use of novel statistical techniques in machine learning and deep networks. Such data-driven techniques allow one to learn model features and characteristics that are difficult for mathematical methods alone to reveal. Many computational methods achieve model and complexity discovery using methods that lie at the nexus of mathematical, statistical, and computational disciplines. Statistical methods often employ “big data” approaches that glean predictive capability from diverse and enormous databases of information. Emerging methods in machine learning and deep networks can provide impressive results. This workshop gathers researchers at the frontier of large-scale statistical computation, data science, tensor decompositions and approximations, and data-driven model learning, to focus on modern challenges that use data to reduce complexity of models.

Organizing Committee

• Lexin Li
• Youssef Marzouk
• Shari Moskow
• Benjamin Peherstorfer
• Abel Rodriguez
• Daniele Venturi
• Rachel Ward

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Lecture Videos