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Exactly conservative physics-informed neural networks and deep operator networks for dynamical systems

Exactly conservative physics-informed neural networks and deep operator networks for dynamical systems

E. Cardoso-Bihlo
Alex Bihlo
ArXivPDFHTML

Papers citing "Exactly conservative physics-informed neural networks and deep operator networks for dynamical systems"

4 / 4 papers shown
Title
Exact conservation laws for neural network integrators of dynamical systems
Exact conservation laws for neural network integrators of dynamical systems
E. Müller
PINN
91
13
0
23 Sep 2022
Respecting causality is all you need for training physics-informed neural networks
Respecting causality is all you need for training physics-informed neural networks
Sizhuang He
Shyam Sankaran
P. Perdikaris
PINN
CML
AI4CE
138
200
0
14 Mar 2022
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Marc Finzi
Max Welling
A. Wilson
160
195
0
19 Apr 2021
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Lu Lu
Pengzhan Jin
George Karniadakis
229
2,123
0
08 Oct 2019