Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks

26 April 2022

Papers citing "Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks"

5 / 5 papers shown

Title
Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks Spyros Rigas M. Papachristou Theofilos Papadopoulos Fotios Anagnostopoulos Georgios Alexandridis AI4CE 34 21 0 24 Jul 2024
Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems A. Thiruthummal Sergiy Shelyag Eun-Jin Kim 18 2 0 07 Jun 2024
Modular machine learning-based elastoplasticity: generalization in the context of limited data J. Fuhg Craig M. Hamel K. Johnson Reese E. Jones N. Bouklas 24 48 0 15 Oct 2022
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process Thi Nguyen Khoa Nguyen T. Dairay Raphael Meunier Mathilde Mougeot PINN AI4CE 67 29 0 31 Jan 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 40 209 0 16 Jul 2021