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Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium

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npj Computational Materials 2022年第1期8卷 245-254页

作者： Ruiyang Li Jian-Xun Wang eungkyu lee Tengfei Luo Department of Aerospace and Mechanical Engineering University of Notre DameNotre DameIN46556USA Department of Electronic Engineering Kyung Hee UniversityYongin-siGyeonggi-do17104South Korea Department of Chemical and Biomolecular Engineering University of Notre DameNotre DameIN46556USA Center for Sustainable Energy of Notre Dame(ND Energy) University of Notre DameNotre DameIN46556USA

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 *** 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 *** 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 *** experiments suggest that the proposed PINN can accurately predict phonon transport(from 1D to 3D)under arbitrary temperature ***,the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.

关键词： phonon equilibrium equation

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Quantum-inspired genetic algorithm for designing planar multilayer photonic structure

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npj Computational Materials 2024年第1期10卷 525-535页

作者： Zhihao Xu Wenjie Shang Seongmin Kim Alexandria Bobbitt eungkyu lee Tengfei Luo Department of Aerospace and Mechanical Engineering University of Notre DameNotre DameIN46556USA National Center for Computational Sciences Oak Ridge National LaboratoryOak RidgeTN37830USA Department of Electronic Engineering Kyung Hee UniversityYongin-siGyeonggi-do17104Republic of Korea Department of Chemical and Biomolecular Engineering University of Notre DameNotre DameIN46556USA

Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search *** to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be *** propose a novel optimization strategy based on an active learning scheme that combines the Quantum-inspired Genetic Algorithm(QGA)with machine learning surrogate model *** Random Forests as the surrogate model circumvents the time-consuming physical modeling or experiments,thereby improving the optimization ***,a genetic algorithm embedded with quantum mechanics,combines the advantages of quantum computing and genetic algorithms,enabling faster and more robust convergence to the *** the design of planar multilayer photonic structures for transparent radiative cooling as a testbed,we show superiority of our algorithm over the classical genetic algorithm(CGA).Additionally,we show the precision advantage of the Random Forest(RF)model as a flexible surrogate model,which relaxes the constraints on the type of surrogate model that can be used in other quantum computing optimization algorithms(e.g.,quantum annealing needs Ising model as a surrogate).

关键词： Quantum algorithm multilayer

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