Physics-informed neural networks for solving forward and inverse
problems in complex beam systems

v1v2 (latest)

Physics-informed neural networks for solving forward and inverse problems in complex beam systems

2 March 2023

ArXiv (abs)PDF HTML

Papers citing "Physics-informed neural networks for solving forward and inverse problems in complex beam systems"

9 / 9 papers shown

Title
Solving 2-D Helmholtz equation in the rectangular, circular, and elliptical domains using neural networks D. Veerababu Prasanta K. Ghosh 104 1 0 26 Mar 2025
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale Michael G. Kapteyn Jacob V. R. Pretorius Karen E. Willcox 55 226 0 10 Dec 2020
Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks Pengzhan Jin Zhen Zhang Ioannis G. Kevrekidis George Karniadakis 73 50 0 05 Dec 2020
Physics Guided Machine Learning Methods for Hydrology A. Khandelwal Shaoming Xu Xiang Li X. Jia M. Steinbach C. Duffy John L. Nieber Vipin Kumar AI4CE 43 38 0 02 Dec 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 135 914 0 28 Jul 2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs Siddhartha Mishra Roberto Molinaro PINN 68 174 0 29 Jun 2020
BayesFlow: Learning complex stochastic models with invertible neural networks Stefan T. Radev U. Mertens A. Voss Lynton Ardizzone Ullrich Kothe BDL 288 197 0 13 Mar 2020
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling Arka Daw Anuj Karpatne William Watkins J. Read Vipin Kumar PINN 42 536 0 31 Oct 2017
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations M. Raissi George Karniadakis AI4CE PINN 75 1,137 0 02 Aug 2017