nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications

8 April 2020

Papers citing "nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications"

33 / 33 papers shown

Title
Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module Jeesuk Shin Cheolwoong Kim Sunwoong Yang Minseo Lee S. J. Kim J. Jeon 74 0 0 23 Apr 2025
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs Zhenao Song 111 0 0 17 Apr 2025
UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs Xi Han Fei Hou Hong Qin AI4CE 65 2 0 09 Aug 2024
Separable Physics-Informed Neural Networks for the solution of elasticity problems V. A. Es'kin Danil V. Davydov Julia V. Guréva Alexey O. Malkhanov Mikhail E. Smorkalov PINN AI4CE 81 3 0 24 Jan 2024
Approximation of Solution Operators for High-dimensional PDEs Nathan Gaby Xiaojing Ye 73 0 0 18 Jan 2024
Learning About Structural Errors in Models of Complex Dynamical Systems Jin-Long Wu Matthew E. Levine Tapio Schneider Andrew M. Stuart AI4CE 82 18 0 29 Dec 2023
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology Qingsong Xu Yilei Shi Jonathan Bamber Ye Tuo Ralf Ludwig Xiao Xiang Zhu AI4CE 133 10 0 08 Oct 2023
$PMNN:Physical Model-driven Neural Network for solving time-fractional differential equations$ PMNN:Physical Model-driven Neural Network for solving time-fractional differential equations Zhiying Ma Jie Hou Wenhao Zhu Yaxin Peng Ying Li 103 13 0 07 Oct 2023
Approximating High-Dimensional Minimal Surfaces with Physics-Informed Neural Networks Steven Zhou Xiaojing Ye PINN 52 0 0 05 Sep 2023
Reconstructing $S$ -matrix Phases with Machine Learning Aurélien Dersy M. Schwartz A. Zhiboedov 40 5 0 18 Aug 2023
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 90 4 0 04 Aug 2023
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data Songming Liu Zhongkai Hao Chengyang Ying Hang Su Ze Cheng Jun Zhu AI4CE 65 12 0 30 May 2023
About optimal loss function for training physics-informed neural networks under respecting causality V. A. Es'kin Danil V. Davydov Ekaterina D. Egorova Alexey O. Malkhanov Mikhail A. Akhukov Mikhail E. Smorkalov PINN 93 7 0 05 Apr 2023
Neural Control of Parametric Solutions for High-dimensional Evolution PDEs Nathan Gaby X. Ye Haomin Zhou 64 6 0 31 Jan 2023
MC-Nonlocal-PINNs: handling nonlocal operators in PINNs via Monte Carlo sampling Xiaodong Feng Yue Qian W. Shen 57 3 0 26 Dec 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 123 98 0 15 Nov 2022
An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator A. Mavi A. Bekar E. Haghighat E. Madenci 84 28 0 21 Oct 2022
AutoKE: An automatic knowledge embedding framework for scientific machine learning Mengge Du Yuntian Chen Dongxiao Zhang AI4CE 75 11 0 11 May 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 64 63 0 16 Mar 2022
State-of-the-Art Review of Design of Experiments for Physics-Informed Deep Learning Sourav Das S. Tesfamariam PINN AI4CE 79 20 0 13 Feb 2022
Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions Kamil Doleglo Anna Paszyñska Maciej Paszyñski L. Demkowicz 19 5 0 03 Jan 2022
An extended physics informed neural network for preliminary analysis of parametric optimal control problems N. Demo M. Strazzullo G. Rozza PINN 66 36 0 26 Oct 2021
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby PINN AI4CE 105 45 0 26 Oct 2021
A data-driven peridynamic continuum model for upscaling molecular dynamics Huaiqian You Yue Yu Stewart Silling M. DÉlia AI4CE 68 46 0 04 Aug 2021
DySMHO: Data-Driven Discovery of Governing Equations for Dynamical Systems via Moving Horizon Optimization F. Lejarza M. Baldea AI4CE 79 39 0 30 Jul 2021
Meta-learning PINN loss functions Apostolos F. Psaros Kenji Kawaguchi George Karniadakis PINN 95 106 0 12 Jul 2021
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs) Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby 99 46 0 25 Jun 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 117 153 0 22 Dec 2020
Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws Huaiqian You Yue Yu Stewart Silling M. DÉlia 95 32 0 08 Dec 2020
Graph Convolutional Neural Networks for Body Force Prediction Francis Ogoke Kazem Meidani Amirreza Hashemi A. Farimani GNN AI4CE 41 62 0 03 Dec 2020
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks Xiaoli Chen Liu Yang Jinqiao Duan George Karniadakis 94 83 0 24 Aug 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 143 928 0 28 Jul 2020
Data-driven learning of robust nonlocal physics from high-fidelity synthetic data Huaiqian You Yue Yu Nathaniel Trask Mamikon A. Gulian M. DÉlia 56 35 0 17 May 2020