Embedding a differentiable Fourier solver as a low-fidelity physics core lets a neural surrogate predict phonon-BTE conductivity of porous nanostructures to ~5% error with 300 BTE simulations and design targets at ~4% average error.
Comparison of the performances between the gray and non-gray media approaches of thermal transport in silicon-tin
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Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation
Embedding a differentiable Fourier solver as a low-fidelity physics core lets a neural surrogate predict phonon-BTE conductivity of porous nanostructures to ~5% error with 300 BTE simulations and design targets at ~4% average error.