most citedLSTM-PINN: An Hybrid Method for Prediction of Steady-State Electrohydrodynamic Flow

6 citations · 6 across the 3 of their papers we have counts for

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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★ 6 cited

LSTM-PINN: An Hybrid Method for Prediction of Steady-State Electrohydrodynamic Flow

Ze Tao, Ke Xu, Fujun Liu

Physics-Informed Neural Networks (PINNs) have demonstrated considerable success in solving complex fluid dynamics problems. However, their performance often deteriorates in regimes…