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DTSTART;TZID=Europe/Stockholm:20230626T173000
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UID:submissions.pasc-conference.org_PASC23_sess163_msa198@linklings.com
SUMMARY:Predictive Scale-Bridging Simulations through Active Learning
DESCRIPTION:Minisymposium\n\nTimothy Germann (Los Alamos National Laborato
 ry)\n\nThroughout computational science, there is a growing need to utiliz
 e the continual improvements in raw computational horsepower to achieve gr
 eater 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 tig
 ht 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 transp
 ort and mixing without truly accounting for molecular interactions. With t
 hese two disparate applications in mind, we develop a novel capability whi
 ch uses an active learning approach to optimize the use of local fine-scal
 e simulations for informing coarse-scale hydrodynamics. Our approach addre
 sses three challenges: forecasting continuum coarse-scale trajectory to sp
 eculatively execute new fine-scale molecular dynamics calculations, dynami
 cally updating coarse-scale from fine-scale calculations, and quantifying 
 uncertainty in neural network models.\n\nDomain: Computer Science, Machine
  Learning, and Applied Mathematics 
\n\nSession Chairs: Riccardo Balin (Ar
 gonne National Laboratory) and Ramesh Balakrishnan (Argonne National Labor
 atory)
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