96 citations · 133 across the 7 of their papers we have counts for
8 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…
Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)
Shamsulhaq Basir
This paper explores the difficulties in solving partial differential equations (PDEs) using physics-informed neural networks (PINNs). PINNs use physics as a regularization term in…
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…
Critical Investigation of Failure Modes in Physics-informed Neural Networks
Shamsulhaq Basir, Inanc Senocak
Several recent works in scientific machine learning have revived interest in the application of neural networks to partial differential equations (PDEs). A popular approach is to a…
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion
Shamsulhaq Basir, Inanc Senocak
Physics-informed neural networks (PINNs) have been proposed to learn the solution of partial differential equations (PDE). In PINNs, the residual form of the PDE of interest and it…