Webinars
All webinars start at 12:00 p.m. PT/3:00 p.m. ET unless otherwise noted.
Webinar recordings for the Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PhILMs) Center can be found on YouTube.
2025
February 25, 2025
"Solving Sparse Finite Element Problems on Neuromorphic Hardware"
Brad Aimone and Brad Theilman, Sandia National Laboratories
January 14, 2025
"LatticeGraphNet: a Two-Scale Graph Neural Operator for Simulating Lattices and Structures"
Ehsan Haghighat, Massachusetts Institute of Technology
January 7, 2025
"Model Aggregation: Data-driven Combination of Any Prediction"
Theo Bourdais, CalTech
2024
November 5, 2024
"HJ-sampler: a Bayesian Sampler for Inverse Problems of a Stochastic Process by Leveraging Hamilton--Jacobi PDEs and Score-based Generative Models"
Tingwei Meng, University of California Los Angeles &
Zongren Zou, Brown University
October 29, 2024
"Generative Machine Learning Models for Uncertainty Quantification"
Guannan Zhang
Oak Ridge National Laboratory
September 24, 2024 at 9:00 a.m. PT
"On Foundation Models for PDEs"
Siddhartha Mishra
ETH Zurich
September 10, 2024 at 10:00 a.m. PT
"Aurora: A Foundation Model of the Atmosphere"
Paris Perdikaris
University of Pennsylvania
May 21, 2024
"Multi-Stage Neural Networks Achieving Machine Precision"
Ching-Yao Lai
Stanford University
February 20, 2024
"A Fast and Interpretable Training Framework for Spiking Neural Networks"
Haiping Huang
Sun Yat-sen University
February 6, 2024
"Challenges in Causal Representation Learning and Deep Generative Models"
Bryon Aragam
University of Chicago
January 2, 2024
"Structure Preservation in Immersed Finite Element Methods for Incompressible Fluid Flow"
John Evans
University of Colorado, Boulder
2023
September 12, 2023
"Exact and Approximate Symmetries in Machine Learning Models"
Soledad Villar
Johns Hopkins University
August 15, 2023
"The Metriplectic 4-bracket: A Curvature-like Framework for Describing Dissipation in Joined Hamiltonian and Dissipative Fluid and Plasma"
Philip Morrison
The University of Texas at Austin
August 1, 2023
"Role of Physics in Physics-Informed Machine Learning"
Daniel Tartakovsky
Stanford University
July 11, 2023
"Computational Hypergraph Discovery"
Houman Owhadi
CalTech
June 27, 2023
"GREAD: Graph Neural Reaction-Diffusion Networks"
Noseong Park
Yonsei University
June 20, 2023
"Machine Learning Constitutive Models of Inelastic Materials with Microstructure"
Reese Jones
Sandia National Laboratories
June 13, 2023
"Uncertainty Quantification and Trustworthy AI-assisted Decision Making"
Prasanna Sattigeri
IBM
May 30, 2023
"Stochastic Graph Embeddings on Transformers in Dynamic Graphs"
Mengjia Xu
Brown University
May 23, 2023
"Multifidelity Scientific Machine Learning"
Panos Stinis
Pacific Northwest National Laboratory
May 16, 2023
"Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications"
Prasanna Balaprakash
Oak Ridge National Laboratory
May 2, 2023
"Bio-Plausible Algorithm-Hardware Co-Design for Efficient & Robust AI"
Priya Panda
Yale University
April 25, 2023
"Scale up Your In-Memory Computing: A Heterogenous Paradigm"
Yu (Kevin) Cao
Arizona State University
April 18, 2023
"Uncertainty Quantification for Deep Learning of Complex Systems"
Eric Hall
Dundee University
April 4, 2023
"Building a Smart Ocean Observational Framework: Challenges and Opportunities for Robotics and Autonomous Systems"
Ani Hsieh
University of Pennsylvania
March 28, 2023
“GFlowNet: Introduction and Applications to AI-Driven Scientific Discovery”
Alex Hernandez-Garcia
Mila-Quebec Artificial Intelligence Institute