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

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

8 April 2020

George Karniadakis

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

15 / 15 papers shown

Title
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems Sumanth Kumar Boya Deepak Subramani AI4CE 99 0 0 12 Dec 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 27 2 0 24 Jan 2024
Approximation of Solution Operators for High-dimensional PDEs Nathan Gaby Xiaojing Ye 30 0 0 18 Jan 2024
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 20 10 0 08 Oct 2023
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 38 4 0 04 Aug 2023
$Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations$ 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 29 58 0 16 Mar 2022
An extended physics informed neural network for preliminary analysis of parametric optimal control problems N. Demo M. Strazzullo G. Rozza PINN 31 33 0 26 Oct 2021
DySMHO: Data-Driven Discovery of Governing Equations for Dynamical Systems via Moving Horizon Optimization F. Lejarza M. Baldea AI4CE 27 38 0 30 Jul 2021
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs) Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby 29 42 0 25 Jun 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
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 8 80 0 24 Aug 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 33 878 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 17 35 0 17 May 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 371 11,700 0 09 Mar 2017
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,327 0 05 Nov 2016