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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

10 January 2024
Jochen Stiasny
Spyros Chatzivasileiadis
    PINN
ArXivPDFHTML

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

3 / 3 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
45
15
0
31 Mar 2022
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
200
2,108
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
PINN
AI4CE
ODL
148
2,796
0
20 Feb 2015
1