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physics.flu-dyn2026
Learnable Viscosity Modulation in Physics-Informed Neural Networks for Incompressible Flow Reconstruction
Ke Xu, Ze Tao, Fujun Liu
Accurately and stably solving the incompressible Navier--Stokes equations with physics-informed neural networks (PINNs) remains challenging, particularly for sparse or noisy observ…
physics.flu-dyn2025
Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks
Fulin Xing, Junjie Li, Ze Tao +2
Underwater explosion modeling faces a critical challenge of simultaneously resolving shock waves and gas-liquid interfaces, as traditional methods struggle to balance accuracy and…