Machine learning visualization tool for exploring parameterized hydrodynamics

Jekel, C. F.; Sterbentz, D. M.; Stitt, T. M.; Mocz, P.; Rieben, R. N.; White, D. A.; Belof, J. L.

doi:10.1088/2632-2153/ad8daa

Title: Machine learning visualization tool for exploring parameterized hydrodynamics

Journal Article · 21 November 2024 · Machine Learning: Science and Technology

DOI: https://doi.org/10.1088/2632-2153/ad8daa · OSTI ID:2479017

^[1]; Sterbentz, D. M. ^[1]; Stitt, T. M. ^[1];

^[1]; Rieben, R. N. ^[1]; White, D. A. ^[1];

^[1]

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding $$\mathscr{O} (TB)$$ of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize 'what-if' situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2479017

Report Number(s):: LLNL--JRNL-865692; 1099349

Journal Information:: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 4 Vol. 5; ISSN 2632-2153

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Machine learning visualization tool for exploring parameterized hydrodynamics

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