3 papers
physics.flu-dyn2026
Unsupervised simulation of incompressible flows with physics- and equality- constrained artificial neural networks
Qifeng Hu, Inanc Senocak
Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations, yet their success in simulating incompressible flows at high Reynolds number…
cs.LG2025
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…
cs.LG2024
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…