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 temperatu...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.展开更多
基金The authors would like to thank ONR MURI(N00014-18-1-2429)for the financial support.The simulations are supported by the Notre Dame Center for Research ComputingNSF through the eXtreme Science and Engineering Discovery Environment(XSEDE)computing resources provided by Texas Advanced Computing Center(TACC)Stampede II under grant number TG-CTS100078This work is also supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1C1C1006251).
文摘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.
