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JAX-BTE: a GPU-accelerated differentiable solver for phonon Boltzmann transport equations

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Published in:NPJ Computational Materials vol. 11, no. 1 (2025), p. 129
Published:
Nature Publishing Group
Subjects:
Boltzmann transport equation
Solvers
Thermal analysis
Semiconductor devices
Graphics processing units
Phonons
Machine learning
Simulation
Programming languages
Accuracy
Physics
Back propagation
Inverse problems
Optimization
Neural networks
Efficiency
Parameter estimation
Online Access:Citation/Abstract
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Abstract:This paper introduces JAX-BTE, a GPU-accelerated, differentiable solver for the phonon Boltzmann Transport Equation (BTE) based on differentiable programming. JAX-BTE enables accurate, efficient and differentiable multiscale thermal modeling by leveraging high-performance GPU computing and automatic differentiation. The solver efficiently addresses the high-dimensional and complex integro-differential nature of the phonon BTE, facilitating both forward simulations and data-augmented inverse simulations through end-to-end optimization. Validation is performed across a range of 1D to 3D simulations, including complex FinFET structures, in both forward and inverse settings, demonstrating excellent performance and reliability. JAX-BTE significantly outperforms state-of-the-art BTE solvers in forward simulations and uniquely enables inverse simulations, making it a powerful tool for multiscale thermal analysis and design for semiconductor devices.
ISSN:2057-3960
DOI:10.1038/s41524-025-01635-0
Source:Health & Medical Collection