An Expert's Guide to Training Physics-informed Neural Networks

An Expert's Guide to Training Physics-informed Neural Networks

16 August 2023

Papers citing "An Expert's Guide to Training Physics-informed Neural Networks"

18 / 18 papers shown

Title
Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Matthias Höfler Francesco Regazzoni S. Pagani Elias Karabelas Christoph M. Augustin Gundolf Haase Gernot Plank Federica Caforio 17 0 0 06 May 2025
Integration Matters for Learning PDEs with Backwards SDEs Sungje Park Stephen Tu PINN 50 0 0 02 May 2025
Inverse Modeling of Dielectric Response in Time Domain using Physics-Informed Neural Networks Emir Esenov Olof Hjortstam Yuriy Serdyuk Thomas Hammarström Christian Häger 17 0 0 28 Apr 2025
Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble Zongren Zou Zhicheng Wang George Karniadakis PINN AI4CE 65 2 0 08 Mar 2025
Unraveling particle dark matter with Physics-Informed Neural Networks M.P. Bento H.B. Câmara J.F. Seabra 53 0 0 24 Feb 2025
Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics Vineet Jagadeesan Nair PINN 38 0 0 10 Oct 2024
Deep Learning Alternatives of the Kolmogorov Superposition Theorem Leonardo Ferreira Guilhoto P. Perdikaris 38 7 0 02 Oct 2024
Astral: training physics-informed neural networks with error majorants V. Fanaskov Tianchi Yu Alexander Rudikov Ivan V. Oseledets 25 1 0 04 Jun 2024
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation Madison Cooley Shandian Zhe Robert M. Kirby Varun Shankar 54 1 0 04 Jun 2024
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective Sunwoong Yang Hojin Kim Y. Hong K. Yee R. Maulik Namwoo Kang PINN AI4CE 18 17 0 05 Jan 2024
Random Weight Factorization Improves the Training of Continuous Neural Representations Sifan Wang Hanwen Wang Jacob H. Seidman P. Perdikaris 21 9 0 03 Oct 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 37 207 0 16 Jul 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 69 220 0 26 Apr 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 39 489 0 09 Feb 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 131 437 0 18 Dec 2020
$NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework$ NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework O. Hennigh S. Narasimhan M. A. Nabian Akshay Subramaniam Kaustubh Tangsali M. Rietmann J. Ferrandis Wonmin Byeon Z. Fang S. Choudhry PINN AI4CE 91 125 0 14 Dec 2020
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains Ziqi Liu Wei Cai Zhi-Qin John Xu AI4CE 155 122 0 22 Jul 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 117 506 0 11 Mar 2020