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Boltzmann transport equation (BTE) is an ideal tool to describe themultiscale phonon transport phenomena, which are critical to applications likemicroelectronics cooling. Numerically solving phonon BTE is extremelycomputationally challenging due to the high dimensionality of such problems,especially when mode-resolved properties are considered. In this work, wedemonstrate the use of physics-informed neural networks (PINNs) to efficientlysolve phonon BTE for multiscale thermal transport problems with theconsideration of phonon dispersion and polarization. In particular, a PINNframework is devised to predict the phonon energy distribution by minimizingthe residuals of governing equations and boundary conditions, without the needfor any labeled training data. Moreover, geometric parameters, such as thecharacteristic length scale, are included as a part of the input to PINN, whichenables learning BTE solutions in a parametric setting. The effectiveness ofthe present scheme is demonstrated by solving a number of phonon transportproblems in different spatial dimensions (from 1D to 3D). Compared to existingnumerical BTE solvers, the proposed method exhibits superiority in efficiencyand accuracy, showing great promises for practical applications, such as thethermal design of electronic devices.

Physics-Informed Neural Networks for Solving Multiscale Mode-Resolved Phonon Boltzmann Transport Equation