2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
A Generalized Schwarz-type Non-overlapping Domain Decomposition Method using Physics-constrained Neural Networks
Shamsulhaq Basir, Inanc Senocak
We present a meshless Schwarz-type non-overlapping domain decomposition method based on artificial neural networks for solving forward and inverse problems involving partial differ…
cs.LG2023
An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networks
Shamsulhaq Basir, Inanc Senocak
Physics and equality constrained artificial neural networks (PECANN) are grounded in methods of constrained optimization to properly constrain the solution of partial differential…
cs.LG2022★ 2 cited
Characterizing and Mitigating the Difficulty in Training Physics-informed Artificial Neural Networks under Pointwise Constraints
Shamsulhaq Basir, Inanc Senocak
Neural networks can be used to learn the solution of partial differential equations (PDEs) on arbitrary domains without requiring a computational mesh. Common approaches integrate…