An operator preconditioning perspective on training in physics-informed machine learning

9 October 2023

Papers citing "An operator preconditioning perspective on training in physics-informed machine learning"

5 / 5 papers shown

Title
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs Sidharth S. Menon Ameya D. Jagtap PINN 73 0 0 06 May 2025
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning Nilo Schwencke Cyril Furtlehner 64 1 0 14 Dec 2024
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations Taniya Kapoor Hongrui Wang Alfredo Núñez R. Dollevoet AI4CE PINN 6 15 0 01 Nov 2023
Convergence and error analysis of PINNs Nathan Doumèche Gérard Biau D. Boyer PINN AI4CE 26 16 0 02 May 2023
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