Numerical Computation of Partial Differential Equations by Hidden-Layer
Concatenated Extreme Learning Machine

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Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine

24 April 2022

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Papers citing "Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine"

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Title
Adaptive deep density approximation for Fokker-Planck equations Keju Tang Xiaoliang Wan Qifeng Liao 79 40 0 20 Mar 2021
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 923 0 28 Jul 2020
A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks S. Dong Naxian Ni 83 137 0 15 Jul 2020
VAE-KRnet and its applications to variational Bayes Xiaoliang Wan Shuangqing Wei BDL DRL 75 13 0 29 Jun 2020
Growing axons: greedy learning of neural networks with application to function approximation Daria Fokina Ivan Oseledets 71 18 0 28 Oct 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 99 1,544 0 10 Jul 2019
Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations Vikas Dwivedi Balaji Srinivasan PINN 65 193 0 08 Jul 2019
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 125 1,393 0 30 Sep 2017