Physics-Informed Deep Learning for Solving Phonon Boltzmann Transport Equation with Large Temperature Non-Equilibrium

TL;DR

This paper introduces a physics-informed neural network method to solve the phonon Boltzmann transport equation under large temperature gradients, enabling accurate and efficient thermal modeling in complex systems.

Contribution

The paper presents a novel data-free PINN approach that incorporates temperature-dependent phonon relaxation times for solving the BTE with large temperature non-equilibrium.

Findings

Accurately predicts phonon transport in 1D to 3D systems.

Handles arbitrary temperature gradients effectively.

Shows potential for device-level thermal design.

Abstract

Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, but assumptions about the system temperatures (i.e., small temperature gradients) are usually made to ensure that it is computationally tractable. To include the effects of large temperature non-equilibrium, we demonstrate a data-free deep learning scheme, physics-informed neural network (PINN), for solving stationary, mode-resolved phonon BTE with arbitrary temperature gradients. This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport (from 1D to 3D) under arbitrary temperature gradients. Moreover, the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.