The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks

6 February 2024

Papers citing "The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks"

10 / 10 papers shown

Title
Neural Tangent Kernel of Neural Networks with Loss Informed by Differential Operators Weiye Gan Yicheng Li Q. Lin Zuoqiang Shi 39 0 0 14 Mar 2025
Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks Anas Jnini Lorenzo Breschi Flavio Vella AI4CE 47 0 0 02 Mar 2025
Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ? Zijian Zhou Zhenya Yan 95 10 0 09 Dec 2024
Physics-informed kernel learning Nathan Doumèche Francis Bach Gérard Biau Claire Boyer PINN 37 2 0 20 Sep 2024
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks Felix Dangel Johannes Müller Marius Zeinhofer ODL 21 6 0 24 May 2024
Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization Mariia Seleznova Gitta Kutyniok 179 16 0 01 Feb 2022
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 45 209 0 16 Jul 2021
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains Hengjie Wang R. Planas Aparna Chandramowlishwaran Ramin Bostanabad AI4CE 42 61 0 22 Apr 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sifan Wang Hanwen Wang P. Perdikaris 131 438 0 18 Dec 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 180 758 0 13 Mar 2020