1 citations · 1 across the 3 of their papers we have counts for
4 papers
Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems
Qifeng Hu, Shamsulhaq Basir, Inanc Senocak
We present several key advances to the Physics and Equality Constrained Artificial Neural Networks (PECANN) framework, substantially improving its capacity to solve challenging par…
Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural Networks
Qifeng Hu, Shamsulhaq Basir, Inanc Senocak
We present a non-overlapping, Schwarz-type domain decomposition method with a generalized interface condition, designed for physics-informed machine learning of partial differentia…
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…
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…