When and why PINNs fail to train: A neural tangent kernel perspective

28 July 2020

Papers citing "When and why PINNs fail to train: A neural tangent kernel perspective"

22 / 22 papers shown

Title
Dual-Balancing for Physics-Informed Neural Networks Chenhong Zhou Jie Chen Zaifeng Yang Ching Eng Png PINN AI4CE 71 0 0 16 May 2025
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs Sidharth S. Menon Ameya D. Jagtap PINN 255 0 0 06 May 2025
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 86 0 0 25 Apr 2025
PIED: Physics-Informed Experimental Design for Inverse Problems Apivich Hemachandra Gregory Kang Ruey Lau Szu Hui Ng Bryan Kian Hsiang Low PINN 67 0 0 10 Mar 2025
STAF: Sinusoidal Trainable Activation Functions for Implicit Neural Representation Alireza Morsali MohammadJavad Vaez Mohammadhossein Soltani Amirhossein Kazerouni Babak Taati Morteza Mohammad-Noori 287 1 0 02 Feb 2025
MILP initialization for solving parabolic PDEs with PINNs Sirui Li Federica Bragone Matthieu Barreau Kateryna Morozovska 57 0 0 28 Jan 2025
Theoretical characterisation of the Gauss-Newton conditioning in Neural Networks Jim Zhao Sidak Pal Singh Aurelien Lucchi AI4CE 67 0 0 04 Nov 2024
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 51 2 0 04 Oct 2024
Deep Learning Alternatives of the Kolmogorov Superposition Theorem Leonardo Ferreira Guilhoto P. Perdikaris 63 7 0 02 Oct 2024
Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text Cooper Lorsung Amir Barati Farimani AI4CE 100 1 0 02 Oct 2024
On the expressiveness and spectral bias of KANs Yixuan Wang Jonathan W. Siegel Ziming Liu Thomas Y. Hou 58 10 0 02 Oct 2024
SetPINNs: Set-based Physics-informed Neural Networks Mayank Nagda Phil Ostheimer Thomas Specht Frank Rhein Fabian Jirasek Stephan Mandt Marius Kloft Sophie Fellenz PINN 3DPC 72 1 0 30 Sep 2024
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks Youngsik Hwang Dong-Young Lim AI4CE 67 2 0 27 Sep 2024
Deep Learning without Global Optimization by Random Fourier Neural Networks Owen Davis Gianluca Geraci Mohammad Motamed BDL 67 0 0 16 Jul 2024
Equivariant Neural Tangent Kernels Philipp Misof Pan Kessel Jan E. Gerken 75 0 0 10 Jun 2024
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation Rui Zhang Qi Meng Rongchan Zhu Yue Wang Wenlei Shi Shihua Zhang Zhi-Ming Ma Tie-Yan Liu DiffM AI4CE 70 5 0 10 Feb 2023
Understanding and mitigating gradient pathologies in physics-informed neural networks Sizhuang He Yujun Teng P. Perdikaris AI4CE PINN 72 292 0 13 Jan 2020
Towards Understanding the Spectral Bias of Deep Learning Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 59 217 0 03 Dec 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang 43 286 0 13 Feb 2019
Physics-informed deep generative models Yibo Yang P. Perdikaris AI4CE PINN 31 58 0 09 Dec 2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yibo Yang P. Perdikaris AI4CE PINN 73 356 0 09 Nov 2018
On the Spectral Bias of Neural Networks Nasim Rahaman A. Baratin Devansh Arpit Felix Dräxler Min Lin Fred Hamprecht Yoshua Bengio Aaron Courville 76 1,408 0 22 Jun 2018