Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions

18 August 2022

Papers citing "Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions"

9 / 9 papers shown

Title
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 86 375 0 21 Jul 2022
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Keju Tang Xiaoliang Wan Chao Yang 39 115 0 28 Dec 2021
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 76 1,195 0 20 May 2021
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 914 0 28 Jul 2020
On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs Yeonjong Shin Jérome Darbon George Karniadakis PINN 67 79 0 03 Apr 2020
Understanding and mitigating gradient pathologies in physics-informed neural networks Sizhuang He Yujun Teng P. Perdikaris AI4CE PINN 97 296 0 13 Jan 2020
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport Qizhi He D. Barajas-Solano G. Tartakovsky A. Tartakovsky AI4CE 52 264 0 06 Dec 2019
Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions Weinan E Qingcan Wang 56 102 0 01 Jul 2018
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