Predictive scale-bridging simulations through active learning

Satish Karra, Mohamed Mehana, Nicholas Lubbers, Yu Chen, Abdourahmane Diaw, Javier E. Santos, Aleksandra Pachalieva, Robert S. Pavel, Jeffrey R. Haack, Michael McKerns, Christoph Junghans, Qinjun Kang, Daniel Livescu, Timothy C. Germann, Hari S. Viswanathan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

Original languageEnglish
Article number16262
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - Dec 2023

Funding

This work was supported by the Laboratory Directed Research and Development program at Los Alamos National Laboratory (LANL) under Project Number 20190005DR. LANL is operated by Triad National Security, LLC, for the National Nuclear Security Administration of US Department of Energy (Contract No. 89233218CNA000001). We also thank the LANL Institutional Computing Program and CCS-7 Darwin cluster for computational resources. SK thanks Environmental Molecular Sciences Laboratory for support. Environmental Molecular Sciences Laboratory is a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. This work was supported by the Laboratory Directed Research and Development program at Los Alamos National Laboratory (LANL) under Project Number 20190005DR. LANL is operated by Triad National Security, LLC, for the National Nuclear Security Administration of US Department of Energy (Contract No. 89233218CNA000001). We also thank the LANL Institutional Computing Program and CCS-7 Darwin cluster for computational resources. SK thanks Environmental Molecular Sciences Laboratory for support. Environmental Molecular Sciences Laboratory is a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830.

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