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    The Institute for Computational and Experimental Research in Mathematics

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    Welcome to ICERM

    The Institute for Computational and Experimental Research in Mathematics

This Week at ICERM

The Industrialization of SciML
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March 24, 2024
  • 9:00 - 9:30 am EDT
    Efficient optimization for simulation and real-world applications
    11th Floor Lecture Hall
    • Speaker
    • Eytan Bakshy, Meta
    • Session Chair
    • Alireza Doostan, University of Colorado Boulder
    Abstract
    Simulations and physical experiments are often resource intensive, and may take days or weeks to obtain estimates of a particular design under consideration. Bayesian optimization is a popular approach to solving such problems. I will discuss recent advances in Bayesian optimization and open source software developed at Meta to solve complex scientific and engineering problems, including methods for high-dimensional optimization, combinatorial, multi and many-objective optimization, and targeting long-term outcomes with short-term experiments. I will present these methods in the context of applications to inverse design of optical components, concrete formulations, and machine learning systems.
  • 9:40 - 10:10 am EDT
    Physics Informed Machine Learning for Optimized Product Performance
    11th Floor Lecture Hall
    • Speaker
    • Juan Betts, PredictiveIQ
    • Session Chair
    • Alireza Doostan, University of Colorado Boulder
    Abstract
    The maintenance and sustainability profile of machines in industrial applications is of critical importance to their field performance. In many instances machine failure modes are dominated by physics and chemistry-based processes such as fatigue, wear, and corrosion. Moreover, these processes typically have measured data for analytics algorithms to predict these failure events. Physics informed machine learning (PIML) techniques seem ideal for these types of problems given that physics can compensate for the lack of data and the fact the physical-chemical processes dominate these failure mechanics. In this paper, we discuss the development of an engine PIML thermo-mechanical (TMF) PIML model and its corresponding validation with test and field data. This paper also discusses extension of these models to wear and oil health applications.
  • 10:20 - 10:50 am EDT
    Coffee Break
    11th Floor Collaborative Space
  • 10:50 - 11:20 am EDT
    Perspectives and Practice for Scientific Machine Learning in Process Systems: Dynamic Surrogacy and Causal Discovery
    11th Floor Lecture Hall
    • Speaker
    • Jordan Jalving, Pasteur Labs
    • Session Chair
    • Alireza Doostan, University of Colorado Boulder
    Abstract
    Advances in Scientific Machine Learning (SciML) are poised to have profound impact within engineering domains that deal with industrial control and process systems (ICPS). Such systems are characterized by the operation of interconnected physical equipment; complex control and data acquisition systems; and sensor data that is nonlinear, noisy, and multi-dimensional. Consequently, maintaining reliable and predictable operation of an ICPS is challenging and often relies on robust advisory systems and expert operator decisions. SciML methods thus offer unique opportunities to develop new emulation and analysis tools that can ultimately enhance and redefine ICPS operation and decision making activities. This talk presents our SciML developments and experiences in using data-driven surrogates and causal discovery methods within the ICPS paradigm. We first discuss perspectives on the use of dynamic surrogate architectures to develop real-time ICPS emulators and how they facilitate effective simulation life-cycle development versus traditional emulator approaches. We further demonstrate how dynamic surrogate models naturally enable state-of-the-art numerical optimization and model predictive control capabilities. Next, we present challenges and opportunities toward utilizing causal discovery methods in the context of monitoring and fault analysis. We discuss how causal discovery methods provide a natural framework to both assist operators in understanding complex system behavior and to help diagnose root causes of faults without requiring a pre-existing historian of faulty operating conditions. We lastly use a benchmark chemical process dataset (the Tennessee Eastman Process) to demonstrate our results.
  • 11:30 am - 12:00 pm EDT
    When big networks are not enough: physics, multifidelity and kernels
    11th Floor Lecture Hall
    • Speaker
    • Panos Stinis, Pacific Northwest National Laboratory
    • Session Chair
    • Alireza Doostan, University of Colorado Boulder
    Abstract
    Modern machine learning has shown remarkable promise in multiple applications. However, brute force use of neural networks, even when they have huge numbers of trainable parameters, can fail to provide highly accurate predictions for problems in the physical sciences. We present a collection of ideas about how enforcing physics, exploiting multifidelity knowledge and the kernel representation of neural networks can lead to significant increase in efficiency and/or accuracy. Various examples are used to illustrate the ideas.
  • 12:10 - 2:00 pm EDT
    Lunch/Free Time
  • 2:00 - 2:30 pm EDT
    Towards 3D Interactive Design Exploration via Neural Networks
    11th Floor Lecture Hall
    • Speaker
    • Victor Oancea, Dassault Systemes
    • Session Chair
    • Panos Stinis, Pacific Northwest National Laboratory
    Abstract
    Physical simulations are performed at multiple scales covering diverse physical domains. Macro scale continuum simulations at part and assembly level, usually employing traditional numerical techniques such as 3D Finite Elements or Finite Volumes, are widely used for physics-based product design because of their high predictiveness value. However, more often than not these simulations are compute-intensive and require minutes/hours/days to execute. Starting from these computationally expensive 3D models, surrogate models massively reduce execution times to seconds (or interactive times), allowing a high number of parameter evaluations in the design space. However, these surrogate models are often of lower fidelity and only provide limited information through a few scaler KPIs (e.g., max stress, max pressure, max intrusion, etc.). To retain the richness of 3D simulation results while significantly reducing executing time, this paper presents a Neural Networks-based approach with the ultimate aim to enable 3D quasi-interactive design exploration . Multiphysics-multiscale traditional simulations [1] are used as the starting point: a) FEA analyses of structural statics, dynamics, manufacturing, packaging and safety, and b) CFD analyses– are used as Design of Experiments (DOEs) to generate the parametric design data. The data is processed and used to train fast executing neural networks as 3D Surrogates. Neural network algorithms and architectures (deep feed forward networks, recurrent and recursive nets, etc.) [2] are chosen depending on the nature of the physics. The trained neural network models can be deployed in a collaborative design environment for interactive design explorations. The proposed approach extends traditional model surrogates to cover both transient physical responses and 3D fields, which could enable an information rich much more productive environment for product design.
  • 2:40 - 3:10 pm EDT
    Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
    11th Floor Lecture Hall
    • Virtual Speaker
    • Lauren Sanders, NASA
    • Session Chair
    • Panos Stinis, Pacific Northwest National Laboratory
    Abstract
    A goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. Here we present an overview of AI/ML architecture support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss robust remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during offworld medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.
  • 3:20 - 3:50 pm EDT
    Coffee Break
    11th Floor Collaborative Space
  • 3:50 - 4:20 pm EDT
    Physics-infused ML for Environmental Monitoring
    11th Floor Lecture Hall
    • Speaker
    • Haruko Wainwright, MIT
    • Session Chair
    • Panos Stinis, Pacific Northwest National Laboratory
    Abstract
    Environmental monitoring – traditionally relied on collecting point samples – are undergoing transformational changes with new technologies such as remote sensing, in situ sensors and various imaging techniques at different scales. At the same time, environmental simulation capabilities are advancing rapidly, predicting environmental flow and contaminant transport in complex systems and quantifying its uncertainty. However, there are still significant challenges to integrate these multi-type multiscale datasets with model simulations. In particular, these datasets are often indirectly correlated with the variables of interest, and have different scales and accuracies. Simulation results are often not perfect due to natural heterogeneities or fine-scale processes not captured in conceptual models. The Advanced Long-term Environmental Monitoring Systems (ALTEMIS) project aims to establish the new paradigm of long-term monitoring of soil and groundwater by integrating these new technologies through machine learning (ML). This talk highlights the two new developments, involving groundwater flow and contaminant transport simulations. First, I will talk about an emulator based on the Fourier neural operator, considering the uncertainty in subsurface parameters and climate forcing. This emulator aims to enable the off-line assessment of future climate change impacts on residual contaminants. Second, I will introduce a Bayesian hierarchical approach coupled with Gaussian process models to integrate in situ sensor data, groundwater sampling data and ensemble simulations. It enables us to infuse physics –such as flow direction and contaminant mobility – into the spatiotemporal characterization of contaminant plumes. Lastly, I will discuss the pathway to actual deployment with the understanding of environmental regulations and site needs, as well as the feedback mechanism to advance deployable technologies
  • 4:30 - 5:00 pm EDT
    Leveraging Large Language Models for Scientific Machine Learning across Domains
    11th Floor Lecture Hall
    • Speaker
    • Adar Kahana, Brown University
    • Session Chair
    • Panos Stinis, Pacific Northwest National Laboratory
    Abstract
    Large Language Models (LLMs) have rapidly emerged as transformative tools in the landscape of scientific inquiry, heralding a new era of interdisciplinary innovation. Our presentation at MIT will embark on a journey through the evolution and significance of LLMs, starting with a brief overview to lay the foundational understanding necessary for appreciating their impact. We will then navigate towards the specific application of LLMs within the realm of Scientific Machine Learning (SciML), illuminating how machine learning techniques are revolutionizing scientific research by examining the practical approaches and methodologies adopted by researchers to address complex SciML challenges. A focal point of our discussion will be the role of simulations in Mechanical Engineering, where we will showcase examples and scenarios that highlight the superior solutions enabled by machine learning. This exploration will not only provide a comprehensive insight into the current state of play but also set the stage for considering LLMs as dynamic agents of change, surveying the latest and greatest research articles employing those agents. We will then try to break the ceiling of 'agent'/'assistant' by discussing General AI (GenAI) and its burgeoning potential to further empower SciML applications directly, and not as orchestrators. Concluding our session, we will venture into speculative territory, pondering the future landscape of the industry as it is shaped by the guiding hand of LLMs in steering research and development towards uncharted territories of knowledge and innovation.
March 25, 2024
  • 3:30 - 4:00 pm EDT
    Coffee Break
    11th Floor Collaborative Space
March 26, 2024
  • 11:00 am - 12:00 pm EDT
    The proximal Galerkin method
    11th Floor Lecture Hall
    • Thomas Surowiec, SIMULA
  • 12:00 - 1:00 pm EDT
    Long short-term memory (LSTM) neural networks
    Post Doc/Graduate Student Seminar - 11th Floor Conference Room
    • John Carter, Rensselaer Polytechnic Institute
  • 3:30 - 4:00 pm EDT
    Coffee Break
    11th Floor Collaborative Space
March 27, 2024
  • 3:30 - 4:00 pm EDT
    Coffee Break
    11th Floor Collaborative Space
March 28, 2024
  • 3:30 - 4:00 pm EDT
    Coffee Break
    11th Floor Collaborative Space
March 29, 2024
  • 9:30 - 10:30 am EDT
    Hiring
    Professional Development - 11th Floor Lecture Hall
  • 3:30 - 4:00 pm EDT
    Coffee Break
    11th Floor Collaborative Space
March 30, 2024

There are no events currently scheduled for March 30th.

All event times are listed in ICERM local time in Providence, RI (Eastern Daylight Time / UTC-4).

All event times are listed in .

Upcoming Programs

The Ceresa Cycle in Arithmetic and Geometry
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Random Matrices and Applications
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