$Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion$

Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion

3 April 2023

Zhi-Qin John Xu

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Papers citing "Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion"

16 / 16 papers shown

Title
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems Xiongbin Yan Z. Xu Zheng Ma 61 2 0 22 Nov 2022
$Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations$ Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations Ling Guo Hao Wu Xiao-Jun Yu Tao Zhou PINN AI4CE 51 61 0 16 Mar 2022
MIONet: Learning multiple-input operators via tensor product Pengzhan Jin Shuai Meng Lu Lu 69 172 0 12 Feb 2022
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs Lulu Zhang Yaoyu Zhang Yaoyu Zhang Weinan E Z. Xu Zheng Ma AI4CE 52 33 0 08 Jul 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 162 284 0 20 Apr 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 500 2,444 0 18 Oct 2020
Multipole Graph Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 216 395 0 16 Jun 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 233 786 0 13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 168 538 0 11 Mar 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar 202 748 0 07 Mar 2020
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 97 1,538 0 10 Jul 2019
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification Yinhao Zhu N. Zabaras UQCV BDL 111 645 0 21 Jan 2018
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 123 1,387 0 30 Sep 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 91 2,066 0 24 Aug 2017
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis 84 268 0 29 Mar 2017
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 168 2,814 0 20 Feb 2015