LSTM-PINN uses memory mechanisms to preserve consistency across heat, fluid, and electric fields in electrothermal transport, outperforming standard PINNs on complex convective regimes.
Physics informed neural networks for solving inverse thermal wave coupled boundary-value problems.International Journal of Heat and Mass Transfer, 245:126985
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
physics.comp-ph 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.