Tensor-Train Compression of Discrete Element Method Simulation Data

TL;DR

This paper introduces a tensor-train based compression framework tailored for discrete element method simulation data, effectively reducing storage needs for raw and derived datasets through hierarchical tensorization.

Contribution

It presents a novel tensor-train compression approach specifically designed for unstructured DEM simulation data, enhancing data storage efficiency.

Findings

Achieves high compression ratios for DEM datasets.

Effective compression of both raw and derived simulation data.

Hierarchical compression scheme improves data handling.

Abstract

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.