Modeling Heat Conduction with Dual-Dissipative Variables: A Mechanism-Data Fusion Method

TL;DR

This paper introduces a novel data-driven modeling approach for non-Fourier heat conduction that combines thermodynamic principles with deep learning, enabling accurate predictions across multiple transport regimes.

Contribution

It proposes the mechanism-data fusion method integrating Conservation-Dissipation Formalism with machine learning for interpretable, accurate non-Fourier heat conduction modeling.

Findings

Model accurately predicts heat conduction in diffusive, hydrodynamic, and ballistic regimes.

Demonstrates robustness with discontinuous initial conditions.

Provides an interpretable PDE series derived from thermodynamic principles.

Abstract

Many macroscopic non-Fourier heat conduction models have been developed in the past decades based on Chapman-Enskog, Hermite or other small perturbation expansion methods. These macroscopic models have made great success on capturing non-Fourier thermal behaviors in solid materials, but most of them are limited by small Knudsen numbers and incapable of capturing highly non-equilibrium or ballistic thermal transport. In this paper, we provide a new strategy for constructing macroscopic non-Fourier heat conduction modeling, that is, using data-driven deep learning methods combined with non-equilibrium thermodynamics instead of small perturbation expansion. We present the mechanism-data fusion method, an approach that seamlessly integrates the rigorous framework of Conservation-Dissipation Formalism (CDF) with the flexibility of machine learning to model non-Fourier heat conduction. Leveraging the conservation-dissipation principle with dual-dissipative variables, we derive an interpretable series of partial differential equations, fine-tuned through a training strategy informed by data from the phonon Boltzmann transport equation. Moreover, we also present the inner-step operation to narrow the gap from the discrete form to the continuous system. Through numerical tests, our model demonstrates excellent predictive capabilities across various heat conduction regimes, including diffusive, hydrodynamic and ballistic regimes, and displays its robustness and precision even with discontinuous initial conditions.