Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology

16 November 2022

Papers citing "Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology"

7 / 7 papers shown

Title
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 76 0 0 25 Apr 2025
State-space models are accurate and efficient neural operators for dynamical systems Zheyuan Hu Nazanin Ahmadi Daryakenari Qianli Shen Kenji Kawaguchi George Karniadakis Mamba AI4CE 64 10 0 28 Jan 2025
Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN) Chenhao Si Ming Yan AI4CE PINN 33 3 0 05 Jun 2024
Learning Interface Conditions in Domain Decomposition Solvers Ali Taghibakhshi Nicolas Nytko Tareq Uz Zaman S. MacLachlan Luke N. Olson Matthew West AI4CE 38 11 0 19 May 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 37 207 0 16 Jul 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 98 272 0 20 Apr 2021
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 117 506 0 11 Mar 2020