Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium
Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium
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DOI：
10.1038/s41524-022-00712-y
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发表时间：
2022-02-08
影响因子：
9.7
通讯作者：
Luo, Tengfei
中科院分区：
文献类型：
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作者：
Li, Ruiyang;Wang, Jian-Xun;Luo, Tengfei
声子玻耳兹曼输运方程(BTE)是模拟多尺度声子输运的重要工具，它对小型化集成电路的热管理至关重要，但通常对系统温度(即小的温度梯度)进行假设，以确保它在计算上是容易处理的。为了包括大温度非平衡的影响，我们展示了一种无数据的深度学习方案，物理信息神经网络(PINN)，用于求解具有任意温度梯度的定常模式分辨声子BTE。该方案使用依赖于温度的声子弛豫时间，在长度尺度和温度梯度都作为输入变量的参数化空间中学习解。数值实验表明，该方法能够准确地预测任意温度梯度下的声子输运(一维到三维)。此外，所提出的方案在有效模拟器件级声子热传导方面显示出很大的潜力，可用于热设计。
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.