Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)

5 June 2024

Papers citing "Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)"

6 / 6 papers shown

Title
Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models Kaiyuan Tan Peilun Li J. Wang Thomas Beckers AI4CE 24 0 0 24 Apr 2025
BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers Rui Zhang Liang Li Stéphane Lanteri Hao Kang Jiaqi Li 36 0 0 14 Apr 2025
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers Chuqi Chen Qixuan Zhou Yahong Yang Yang Xiang Tao Luo 29 1 0 08 Oct 2024
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 45 209 0 16 Jul 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 180 759 0 13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 125 508 0 11 Mar 2020