论文标题
离散元素方法仿真数据的张量训练压缩
Tensor-Train Compression of Discrete Element Method Simulation Data
论文作者
论文摘要
我们提出了一个基于张量训练（TT）分解的离散科学数据压缩的框架。我们的方法是根据离散元素方法（DEM）模拟处理非结构化输出数据的量身定制的，证明了其在压缩RAW（例如粒子位置和速度）和派生（例如应力和应变）数据集方面的有效性。我们表明，几何驱动的“张力”与TT分解（称为量化TT）产生了分层压缩方案，从而达到了这些DEM数据集中的关键变量的高压缩比。
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.
