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On the approximation of rough functions with deep neural networks
v1v2 (latest)

On the approximation of rough functions with deep neural networks

SeMA Journal (SeMA), 2019
13 December 2019
Tim De Ryck
Siddhartha Mishra
Deep Ray
ArXiv (abs)PDFHTML

Papers citing "On the approximation of rough functions with deep neural networks"

4 / 4 papers shown
Learning WENO for entropy stable schemes to solve conservation laws
Learning WENO for entropy stable schemes to solve conservation laws
Philip Charles
Deep Ray
263
1
0
21 Mar 2024
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward
  non-intrusive Meta-learning of parametric PDEs
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEsFinite elements in analysis and design (FEAD), 2023
Yanlai Chen
Shawn Koohy
PINNAI4CE
265
50
0
27 Mar 2023
Error analysis for deep neural network approximations of parametric
  hyperbolic conservation laws
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws
Tim De Ryck
Siddhartha Mishra
PINN
174
15
0
15 Jul 2022
A Multi-level procedure for enhancing accuracy of machine learning
  algorithms
A Multi-level procedure for enhancing accuracy of machine learning algorithmsEuropean journal of applied mathematics (EJAM), 2019
K. Lye
Siddhartha Mishra
Roberto Molinaro
185
34
0
20 Sep 2019
1