1:09:47

Michael Mahoney - Dynamical systems and machine learning

58:12

DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators.

47:27

Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering

59:56

Anima Anandkumar - Neural operator: A new paradigm for learning PDEs

1:10:36

Physics-Informed Neural Networks (PINNs) - An Introduction - Ben Moseley | Jousef Murad

55:02

Zongyi Li's talk on solving PDEs from data

2:06:19

ICML 2024 Tutorial"Machine Learning on Function spaces #NeuralOperators"

51:22

Rethinking Physics Informed Neural Networks [NeurIPS'21]