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Error estimation for physics-informed neural networks with implicit Runge-Kutta methods

Error estimation for physics-informed neural networks with implicit Runge-Kutta methods

Jochen Stiasny
Spyros Chatzivasileiadis
    PINN
ArXiv (abs)PDFHTML

Papers citing "Error estimation for physics-informed neural networks with implicit Runge-Kutta methods"

4 / 4 papers shown
Title
Certified machine learning: A posteriori error estimation for physics-informed neural networks
Certified machine learning: A posteriori error estimation for physics-informed neural networks
Birgit Hillebrecht
B. Unger
PINN
58
15
0
31 Mar 2022
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks
Christian Moya
Guang Lin
AI4CEPINN
85
39
0
09 Sep 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
248
2,150
0
08 Oct 2019
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINNAI4CEODL
168
2,814
0
20 Feb 2015

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