C–PINN: A Coupled Physics-Informed Neural Network for Solving Parabolic Two-Step Models in Electron–Lattice Thermal Conduction

Zhihong Che, Boyang Liu, Qi Lin, Zhecheng Yang and Liangcai Mei

International Journal of Mathematics and Mathematical Sciences, 2025, vol. 2025, 1-16

Abstract: This paper introduces a coupled physics-informed neural network (C–PINN) framework for simulating electron–lattice thermal conduction. The framework uses an additive Δ-decomposition to couple the electron temperature Te and lattice temperature Tl, with a learnable correction term Δt,x capturing spatial–temporal deviations from thermal equilibrium. Additionally, a residual-balanced and localized adaptive refinement (RLAR) strategy is employed to enhance model accuracy and training efficiency, especially in high-error regions. Numerical experiments demonstrate the model’s robustness across different Knudsen numbers (Kn) and boundary conditions, showing its ability to capture fine-grained temperature deviations and optimize high-error regions through dynamic adaptive sampling.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jijmms:5570716

DOI: 10.1155/ijmm/5570716

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