Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks

5 May 2022

Papers citing "Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks"

9 / 9 papers shown

Title
Integration Matters for Learning PDEs with Backwards SDEs Sungje Park Stephen Tu PINN 79 0 0 02 May 2025
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 41 2 0 04 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 70 1 0 30 Sep 2024
Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks R. Mattey Susanta Ghosh AI4CE 51 1 0 09 May 2024
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations Hongyan Li Song Jiang Wenjun Sun Liwei Xu Guanyu Zhou 45 2 0 04 Mar 2024
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires J. Dabrowski D. Pagendam J. Hilton Conrad Sanderson Dan MacKinlay C. Huston Andrew Bolt Petra Kuhnert PINN 52 17 0 02 Dec 2022
Residual-Quantile Adjustment for Adaptive Training of Physics-informed Neural Network Jiayue Han Zhiqiang Cai Zhiyou Wu Xiang Zhou 73 7 0 09 Sep 2022
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow Adam Subel Yifei Guan Ashesh Chattopadhyay Pedram Hassanzadeh AI4CE 40 42 0 07 Jun 2022
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 50 497 0 09 Feb 2021