We propose a framework for discrete scientific data compression based on the tensor-train (TT) decomposition. Our approach is tailored to handle unstructured output data from discrete element method (DEM) simulations, demonstrating its effectiveness in compressing both raw (e.g. particle position and velocity) and derived (e.g. stress and strain) datasets. We show that geometry-driven "tensorization" coupled with the TT decomposition (known as quantized TT) yields a hierarchical compression scheme, achieving high compression ratios for key variables in these DEM datasets.
@article{arxiv.2210.08399,
title = {Tensor-Train Compression of Discrete Element Method Simulation Data},
author = {Saibal De and Eduardo Corona and Paramsothy Jayakumar and Shravan Veerapaneni},
journal= {arXiv preprint arXiv:2210.08399},
year = {2022}
}