Physics-informed neural network and data-driven modeling of non-Fourier heat transfer in laser-irradiated semiconductor media using bi-Helmhotz nonlocal theory

Abstract

This study develops a hybrid analytical and physics-informed machine learning framework to analyze non-Fourier heat transfer in laser-irradiated semiconductor media. The model integrates bi-Helmholtz nonlocal thermoelasticity with dual length-scale parameters to capture size-dependent mechanical effects, alongside a modified Green–Naghdi heat conduction theory to describe finite-speed thermal wave propagation and relaxation phenomena. The coupled governing equations for displacement, temperature, carrier density, and stress are first solved analytically using a normal-mode approach to obtain benchmark solutions. A physics-informed neural network (PINN) is then constructed by embedding the governing equations and boundary conditions into the learning process, enabling efficient and accurate prediction of multiphysics responses. The proposed approach significantly reduces computational cost while preserving high accuracy. Parametric analysis highlights the strong influence of nonlocal parameters and thermal relaxation on wave propagation and carrier dynamics. The framework offers a robust tool for real-time simulation of laser-induced thermal processes in semiconductor systems.