{"title":"离散元素法模拟数据的张量列车压缩","authors":"Saibal De , Eduardo Corona , Paramsothy Jayakumar , Shravan Veerapaneni","doi":"10.1016/j.jterra.2024.100967","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":50023,"journal":{"name":"Journal of Terramechanics","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tensor-train compression of discrete element method simulation data\",\"authors\":\"Saibal De , Eduardo Corona , Paramsothy Jayakumar , Shravan Veerapaneni\",\"doi\":\"10.1016/j.jterra.2024.100967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":50023,\"journal\":{\"name\":\"Journal of Terramechanics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Terramechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022489824000090\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Terramechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022489824000090","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
我们提出了一种基于张量-列车(TT)分解的离散科学数据压缩框架。我们的方法专为处理离散元法(DEM)模拟的非结构化输出数据而设计,证明了它在压缩原始数据集(如粒子位置和速度)和衍生数据集(如应力和应变)方面的有效性。我们展示了几何驱动的 "张量化 "与 TT 分解(称为量化 TT)相结合产生的分层压缩方案,为这些 DEM 数据集中的关键变量实现了高压缩比。
Tensor-train compression of discrete element method simulation data
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.
期刊介绍:
The Journal of Terramechanics is primarily devoted to scientific articles concerned with research, design, and equipment utilization in the field of terramechanics.
The Journal of Terramechanics is the leading international journal serving the multidisciplinary global off-road vehicle and soil working machinery industries, and related user community, governmental agencies and universities.
The Journal of Terramechanics provides a forum for those involved in research, development, design, innovation, testing, application and utilization of off-road vehicles and soil working machinery, and their sub-systems and components. The Journal presents a cross-section of technical papers, reviews, comments and discussions, and serves as a medium for recording recent progress in the field.