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DeepSPoC: A Deep Learning-Based PDE Solver Governed by Sequential Propagation of Chaos

DeepSPoC: A Deep Learning-Based PDE Solver Governed by Sequential Propagation of Chaos

Kai Du
Yongle Xie
Tao Zhou
Yuancheng Zhou
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Papers citing "DeepSPoC: A Deep Learning-Based PDE Solver Governed by Sequential Propagation of Chaos"

5 / 5 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
39
15
0
31 Mar 2022
Adaptive deep density approximation for Fokker-Planck equations
Adaptive deep density approximation for Fokker-Planck equations
Keju Tang
Xiaoliang Wan
Qifeng Liao
60
37
0
20 Mar 2021
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs
Siddhartha Mishra
Roberto Molinaro
PINN
43
172
0
29 Jun 2020
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPM
AI4CE
139
1,662
0
05 Dec 2019
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
E. Weinan
Ting Yu
100
1,373
0
30 Sep 2017

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