LSTM-PINN uses memory mechanisms to preserve consistency across heat, fluid, and electric fields in electrothermal transport, outperforming standard PINNs on complex convective regimes.
Analyticalandneural network approaches for solving two-dimensional nonlinear transient heat conduction
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RA-PINN embeds gated attention in a residual network to reduce localized errors at steep charge boundaries while obeying the governing equations.
LNN-PINN integrates liquid residual blocks into PINNs and reports lower RMSE and MAE on four benchmark problems while leaving the original physics modeling and optimization pipeline unchanged.
citing papers explorer
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LSTM-PINN for Steady-State Electrothermal Transport: Preserving Multi-Field Consis tency in Strongly Coupled Heat and Fluid Flow
LSTM-PINN uses memory mechanisms to preserve consistency across heat, fluid, and electric fields in electrothermal transport, outperforming standard PINNs on complex convective regimes.
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High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs
RA-PINN embeds gated attention in a residual network to reduce localized errors at steep charge boundaries while obeying the governing equations.
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LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
LNN-PINN integrates liquid residual blocks into PINNs and reports lower RMSE and MAE on four benchmark problems while leaving the original physics modeling and optimization pipeline unchanged.