mlsanityData to 14 Oct 2026

Machine Learning Visualization Tool for Exploring Parameterized Hydrodynamics

arXiv 2406.15509 ↗First posted 20 Jun 2024physics.comp-ph cs.LG physics.flu-dyncited by 4

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Abstract (arXiv)

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 Oleft(rm TBright) 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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 16 of its 67 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

16 of 67 references.

References · 16

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4 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energycited by 1
  2. 2026Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systemscited by 1
  3. 2026Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modelingcited by 1
  4. 2025Spatio-temporal, multi-field deep learning of shock propagation in meso-structured mediacited by 0