Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
Fluid flows are governed by the nonlinear Navier-Stokes equations, which can manifest multiscale dynamics even from predictable initial conditions. Predicting such phenomena remains a formidable challenge in scientific machine learning, particularly regarding convergence speed, data requirements, and solution accuracy. In complex fluid flows, these challenges are exacerbated by long-range spatial dependencies arising from distant boundary conditions, which typically necessitate extensive supervision data to achieve acceptable results. We propose the Domain-Decomposed and Shifted Physics-Informed Neural Network (DDS-PINN), a framework designed to resolve such multiscale interactions with m...
| Authors | Prashant Kumar, Rajesh Ranjan |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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