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DOE OSTI · 1878014

Enabling Predictive Scale-Bridging Simulations through Active Learning (Institutional Computing Annual Report (Project w21_alscalebridging)) [Slides]

Germann, Timothy Clark
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Abstract

The goal of this project was to develop, demonstrate, and provide a new capability to achieve greater physical fidelity in large-scale simulations, rather than the usual brute-force increases in the number of mesh elements or particles. Transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, is affected by molecular-level interactions. Several coarse-scale Lattice Boltzmann model (LBM) parameters cannot be directly computed, so instead we calibrate them to molecular dynamics (MD) simulations, by building emulators that mimic MD and LBM behavior and then training an upscaler. The resulting machine learning (ML)-based scale-bridging framework is up to 7 orders of magnitude faster than direct MD.

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74 ATOMIC AND MOLECULAR PHYSICS97 MATHEMATICS AND COMPUTING

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Germann, Timothy Clark. 2022-07-22. Enabling Predictive Scale-Bridging Simulations through Active Learning (Institutional Computing Annual Report (Project w21_alscalebridging)) [Slides]. https://doi.org/10.2172/1878014

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