Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs

20 December 2018

Papers citing "Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs"

6 / 6 papers shown

Title
Approximation theory for 1-Lipschitz ResNets Davide Murari Takashi Furuya Carola-Bibiane Schönlieb 29 0 0 17 May 2025
Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems A. Thiruthummal Sergiy Shelyag Eun-Jin Kim 38 2 0 07 Jun 2024
Residual-based attention and connection to information bottleneck theory in PINNs Sokratis J. Anagnostopoulos Juan Diego Toscano Nikos Stergiopulos George Karniadakis 40 20 0 01 Jul 2023
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs Songming Liu Zhongkai Hao Chengyang Ying Hang Su Jun Zhu Ze Cheng AI4CE 28 17 0 06 Oct 2022
PCNN: A physics-constrained neural network for multiphase flows Haoyang Zheng Ziyang Huang Guang Lin PINN 33 8 0 18 Sep 2021
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 48 211 0 16 Jul 2021