Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains
In this work, viscous fluid flow governed by the Stokes equations in highly perforated domains is studied using physics-informed neural networks (PINNs). Perforated microstructures induce complex boundary conditions and fine-scale flow features that are difficult for standard neural networks to resolve. Conventional PINNs, even when combined with advanced training techniques, can suffer from a loss of accuracy and efficiency as the number of perforations increases. One important source of this difficulty is the soft enforcement of boundary conditions through penalty terms, which can lead to stiffness, gradient conflicts, and poor resolution of near-boundary flow structures. Hard constrain...
| Authors | Jeeeun Lee, Denis Korolev, Miro Duhovic et al. |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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