ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

15 June 2023

Papers citing "ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations"

5 / 5 papers shown

Title
Can Physics Informed Neural Operators Self Improve? Ritam Majumdar Amey Varhade Shirish S. Karande L. Vig AI4CE 17 0 0 23 Nov 2023
Enhancing Convergence Speed with Feature-Enforcing Physics-Informed Neural Networks: Utilizing Boundary Conditions as Prior Knowledge for Faster Convergence Mahyar Jahaninasab M. A. Bijarchi 11 0 0 17 Aug 2023
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving Junjun Yan Xinhai Chen Zhichao Wang Enqiang Zhou Jie Liu PINN AI4CE 24 1 0 12 Jul 2023
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 59 59 0 23 May 2022
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 172 758 0 13 Mar 2020