Reconstruction of heat relaxation index in phonon transport equation

TL;DR

This paper develops a method to reconstruct the relaxation time in phonon transport equations for nano-materials, using inverse PDE problems and stochastic optimization, revealing the breakdown of Fourier's law at nano-scales.

Contribution

It introduces a PDE-constrained optimization approach with SGD to infer relaxation times, accounting for nano-structure effects in heat transport models.

Findings

Successfully reconstructs relaxation time from temperature response data.

Numerically verifies the breakdown of Fourier's law at nano-scales.

Provides a new inverse problem framework for phonon transport modeling.

Abstract

For nano-materials, heat conductivity is an ill-defined concept. This classical concept assumes the validity of Fourier's law, which states the heat flux is proportional to temperature gradient, with heat conductivity used to denote this ratio. However, this macroscopic constitutive relation breaks down at nano-scales. Instead, heat is propagated using phonon transport equation, an ab initio model derived from the first principle. In this equation, a material's thermal property is coded in a coefficient termed the relaxation time ($\tau$). We study an inverse problem in this paper, by using material's temperature response upon heat injection to infer the relaxation time. This inverse problem is formulated in a PDE-constrained optimization, and numerically solved by Stochastic Gradient Descent (SGD) method and its variants. In the execution of SGD, Fr\'echet derivative is computed and Lipschitz continuity is proved. This approach, in comparison to the earlier studies, honors the nano-structure of of heat conductivity in a nano-material, and we numerically verify the break down of the Fourier's law.