Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

1 November 2021

Papers citing "Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems"

39 / 139 papers shown

Title
Physics-informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media Salah A. Faroughi Ramin Soltanmohammad Pingki Datta S. K. Mahjour S. Faroughi 21 22 0 17 Dec 2022
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 23 34 0 06 Dec 2022
On the Compatibility between Neural Networks and Partial Differential Equations for Physics-informed Learning Kuangdai Leng Jeyan Thiyagalingam PINN 26 3 0 01 Dec 2022
Physics-informed Neural Networks with Unknown Measurement Noise Philipp Pilar Niklas Wahlström PINN 23 6 0 28 Nov 2022
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications Zhongkai Hao Songming Liu Yichi Zhang Chengyang Ying Yao Feng Hang Su Jun Zhu PINN AI4CE 35 89 0 15 Nov 2022
Gradient-enhanced deep neural network approximations Xiaodong Feng Li Zeng UQCV 26 5 0 08 Nov 2022
Data-driven modeling of Landau damping by physics-informed neural networks Yilan Qin Jiayu Ma M. Jiang C. Dong H. Fu Liang Wang Wen-jie Cheng Yaqiu Jin PINN AI4CE 13 7 0 02 Nov 2022
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks Shibo Li Michael Penwarden Yiming Xu Conor Tillinghast Akil Narayan Robert M. Kirby Shandian Zhe AI4CE 18 4 0 23 Oct 2022
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency Weilong Guan Kai-Ping Yang Yinsheng Chen Zhong Guan PINN AI4CE 18 12 0 19 Oct 2022
Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks Wei Peng Wenjuan Yao Weien Zhou Xiaoya Zhang Weijie Yao PINN 53 5 0 19 Oct 2022
Asymptotic-Preserving Neural Networks for hyperbolic systems with diffusive scaling Giulia Bertaglia AI4CE 24 5 0 17 Oct 2022
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs Birgit Hillebrecht B. Unger PINN 13 5 0 07 Oct 2022
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems Lianghao Cao Thomas O'Leary-Roseberry Prashant K. Jha J. Oden Omar Ghattas 28 26 0 06 Oct 2022
Failure-informed adaptive sampling for PINNs Zhiwei Gao Liang Yan Tao Zhou 18 77 0 01 Oct 2022
Scaling transformation of the multimode nonlinear Schrödinger equation for physics-informed neural networks I. Chuprov D. Efremenko Jiexing Gao P. Anisimov V. Zemlyakov 24 0 0 29 Sep 2022
GeONet: a neural operator for learning the Wasserstein geodesic Andrew Gracyk Xiaohui Chen OT 21 2 0 28 Sep 2022
Residual-Quantile Adjustment for Adaptive Training of Physics-informed Neural Network Jiayue Han Zhiqiang Cai Zhiyou Wu Xiang Zhou 44 7 0 09 Sep 2022
MRF-PINN: A Multi-Receptive-Field convolutional physics-informed neural network for solving partial differential equations Shihong Zhang Chi Zhang Bo Wang AI4CE 24 3 0 06 Sep 2022
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu Min Zhu Qinyan Tan Yadhu Kartha Lu Lu 29 352 0 21 Jul 2022
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations Junho Choi N. Kim Youngjoon Hong AI4CE 24 0 0 21 Jul 2022
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method Shahed Rezaei Ali Harandi Ahmad Moeineddin Bai-Xiang Xu Stefanie Reese 21 112 0 27 Jun 2022
Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread Giulia Bertaglia Chuan Lu L. Pareschi Xueyu Zhu AI4CE 11 17 0 25 Jun 2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning Thomas O'Leary-Roseberry Peng Chen Umberto Villa Omar Ghattas AI4CE 32 39 0 21 Jun 2022
Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support M. Mohammadian K. Baker Ferdinando Fioretto PINN AI4CE 15 21 0 21 Jun 2022
Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations Zhi‐Yong Zhang Hui Zhang Li-sheng Zhang Lei‐Lei Guo PINN AI4CE 14 27 0 19 Jun 2022
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations Ramansh Sharma Varun Shankar 37 40 0 19 May 2022
Scalable algorithms for physics-informed neural and graph networks K. Shukla Mengjia Xu N. Trask George Karniadakis PINN AI4CE 72 40 0 16 May 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 38 7 0 15 May 2022
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks Wei Peng Weien Zhou Xiaoya Zhang Wenjuan Yao Zheliang Liu 23 15 0 02 May 2022
RAR-PINN algorithm for the data-driven vector-soliton solutions and parameter discovery of coupled nonlinear equations Shulan Qin Min Li Tao Xu Shaotong Dong 17 9 0 29 Apr 2022
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport Lu Lu R. Pestourie Steven G. Johnson Giuseppe Romano AI4CE 21 102 0 14 Apr 2022
MIONet: Learning multiple-input operators via tensor product Pengzhan Jin Shuai Meng Lu Lu 20 158 0 12 Feb 2022
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Keju Tang Xiaoliang Wan Chao Yang 24 107 0 28 Dec 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 222 0 26 Apr 2021
One-shot learning for solution operators of partial differential equations Priya Kasimbeg Haiyang He Rishikesh Ranade Jay Pathak Lu Lu AI4CE 21 11 0 06 Apr 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 41 494 0 09 Feb 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 30 146 0 22 Dec 2020
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sizhuang He Hanwen Wang P. Perdikaris 131 438 0 18 Dec 2020
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains Ziqi Liu Wei Cai Zhi-Qin John Xu AI4CE 223 122 0 22 Jul 2020