论文标题
物理知识的深度学习，用于求解具有较大温度非平衡的声子Boltzmann传输方程
Physics-Informed Deep Learning for Solving Phonon Boltzmann Transport Equation with Large Temperature Non-Equilibrium
论文作者
论文摘要
声子Boltzmann传输方程（BTE）是建模多尺度声子传输的关键工具，这对于微型化集成电路的热管理至关重要，但是对系统温度（即较小温度梯度）的假设通常是为了确保可计算易于触觉的。为了包括大温度非平衡的影响，我们证明了一种无数据的深度学习方案，物理知识的神经网络（PINN），用于求解具有任意温度梯度的固定，模式分辨的声子BTE。该方案使用依赖温度的声子松弛时间，并在长度尺度和温度梯度被视为输入变量的参数化空间中学习解决方案。数值实验表明，在任意温度梯度下，提出的PINN可以准确预测声子运输（从1D到3D）。此外，提出的方案在有效地模拟设备级的声子热传导方面显示出巨大的希望，并且可以用于热设计。
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.
