LSTM-PINN uses memory mechanisms to preserve consistency across heat, fluid, and electric fields in electrothermal transport, outperforming standard PINNs on complex convective regimes.
Analytical and neural 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.