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Scalable, Efficient and Accelerated Causal Reasoning Operators, Graphs and Spikes for Earth and Embedded Systems (SEA-CROGS)

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  3. Scalable, Efficient and Accelerated Causal Reasoning Operators, Graphs and Spikes for Earth and Embedded Systems (SEA-CROGS)

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

Lab-Level Communications Priority Topics

Computing

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