  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
$\mathcal{O}\left({\rm TB}\right)$ 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.
