DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators.
Similar Tracks
DDPS | ML for Solving PDEs: Neural Operators on Function Spaces by Anima Anandkumar
Inside Livermore Lab
Learning operators using deep neural networks for multiphysics, multiscale, & multifidelity problems
Ben Erichson
DeepXDE: A Deep Learning Library for Solving Differential Equations by Lu Lu
MLPS - Combining AI and ML with Physics Sciences
Anima Anandkumar - Neural operator: A new paradigm for learning PDEs
Physics Informed Machine Learning
"Introduction to physics-informed neural networks" Liu Yang (Brown) - CFPU SMLI
Brown University Department of Physics
DDPS | Deep neural operators with reliable extrapolation for multiphysics & multiscale problems
Inside Livermore Lab
Physics-Informed Neural Networks (PINNs) - An Introduction - Ben Moseley | Jousef Murad
Jousef Murad LITE