Participants

Pau Batlle-Franch, California Institute of Technology

Kaushik Bhattacharya, California Institute of Technology

David Bortz, University of Colorado Boulder

Le Cao, University of Kentucky

Matthiew Darcy, California Institute of Technology
Benchmark operator learning with simple and interpretable kernal methods

Saibal De, Sandia National Laboratories
Improving accuracy of stochastic collocation surrogates for ODE/PDE systems via data-driven factorization of model dynamics

Alireza Doostan, University of Colorado Boulder

Mostafa Faghih Shojaei, University of Michigan

Ionut Farcas, The University of Texas at Austin
Parametric non-intrusive reduced-order models via operator inference for large-scale rotating detonation engine simulations

Krishna Garikipati, University of Michigan
Graph calculus neural networks

Gianluca Geraci, Sandia National Laboratories

Roger Ghanem, University of Southern California

Andrew Glaws, National Renewable Energy Laboratory
Scientific machine learning benchmarks to advance wind energy

Alex Gorodetsky, University of Michigan
Methods and approaches to benchmark data-driven modeling in the sparse and noisy data regimes

Somdatta Goswami, Brown University
Solving partial differential equations with machine learning

Anthony Gruber, Sandia National Laboratories
Canonical and noncanonical Hamiltonian model reduction through benchmark examples

Jayesh Gupta, Microsoft
PDEArena: A modern, scalable, and easy to use PDE surrogate benchmarking framework

Joseph Hart, Sandia National Laboratories
Solving inverse problems via neural network flow map approximation

Jamie Holber, University of Michigan

Md Nurtaj Hossain, University of Southern California

Daniel Huang, California Institute of Technology

Opal Issan, University of California, San Diego
Predicting solar wind streams from the inner-heliosphere to earth via shifted operator inference

Parisa Khodabakhshi, Lehigh University
Non-intrusive parametric model reduction via data-drive operator inference

Petros Koumoutsakos, Harvard University
Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation

Ellen Kuhl, Stanford University
Constitutive artificial neural networks: Establishing benchmarks for training and validation

Samuel Lanthaler, California Institute of Technology

Dongjin Lee, University of California, San Diego
Multifidelity risk assessments for nonlinear systems under high-dimensional dependent random variables

Emma Lejeune, Boston Univesity
Open access benchmark datasets for assessing deep learning model accuracy and calibration in mechanics

Matthew Levine, California Institute of Technology
Auto-differentiable data assimilation and neural ODEs for learning latent dynamics

Zongyi Li, California Institute of Technology

Elizabeth Livingston, University of Michigan

Romit Maulik, Argonne National Laboratory & Pennsylvania State University

Nick McGreivy, Princeton University
Can ML-for-science research be trusted? A crisis of weak baselines and reporting bias for ML-for-PDE solving

Katarzyna Michalowska, Brown University

Srideep Musuvathy, Sandia National Laboratories

Habib Najm, Sandia National Laboratories

Nicholas Nelsen, California Institute of Technology
Theory-based principles for benchmarking operator learning

Grant Norman, University of Colorado Boulder

Assad Oberai, University of Southern California

Audrey Olivier, University of Southern California

Houman Owhadi, California Institute of Technology
On solving/learing nonlinear PDEs with GPS

Dhruv Patel, Stanford University

Lekha Patel, Sandia National Laboratories
Deep learning large scale aerosol-cloud interactions from satellite imagery

Ravi Patel, Sandia National Laboratories
Error-in-variables modelling for operator learning benchmarks

Abani Patra, Tufts University

Erik Peterson, Pasteur Labs

Paris Perdikaris, University of Pennsylvania

Deep Ray, University of Maryland, College Park
Variationally mimetic operator network with a consistent loss function

Pieterjan Robbe, Sandia National Laboratories
Surrogate modeling of high-dimensional response surfaces with steep gradients

Jacob Seidman, University of Pennsylvania
Dimensionality reduction in operator learning

Elnaz Seylabi, University of Nevada Reno

Fei Sha, Google

Kurtis Shuler, Sandia National Laboratories

Siddhartha Srivastava, University of Michigan
A framework for physics-informed reinforcement learning with applications to cell migration

Vahidullah Tac, Purdue University
Benchmarks for physics-informed data-driven hyperelasticity

Jian-Xun Wang, University of Notre Dame
Multi-resolution PDE-preserved learning for spatiotemporal dynamics

Jacqueline Wentz, University of Colorado Boulder

Dongbin Xiu, The Ohio State University
Data driven modeling of unknown systems with deep neural networks

Yue Yu, Lehigh University
MetaP: How to transfer your hidden physics knowledge between specimens

Leonardo Zepeda-Núñez, Google Research and University of Wisconsin-Madison
Evolve smoothly, fit consistently: Learning smooth latent dynamics For advection-dominated systems