力学研究中"大数据"的启示、应用与挑战

杨强; 孟松鹤; 仲政; 解维华; 郭早阳; 金华; 张幸红

doi:10.6052/1000-0992-19-002

力学研究中"大数据"的启示、应用与挑战

doi: 10.6052/1000-0992-19-002

杨强^1,2,
孟松鹤^1,†, ,,
仲政³,
解维华¹,
郭早阳³,
金华¹,
张幸红¹

¹ 哈尔滨工业大学特种环境复合材料国家级重点实验室, 哈尔滨150001
² 哈尔滨工业大学材料科学与工程博士后流动站, 哈尔滨 150001
³ 哈尔滨工业大学(深圳)理学院, 深圳 518055

基金项目:

国家自然科学基金项目 (11842013) ，中国博士后科学基金资助项目 (2018M641815)

详细信息

作者简介:
杨强, 1989年出生,哈尔滨工业大学材料科学与工程在站博士后, 助理研究员.主要从事极端环境下材料与结构的设计,及其力学行为建模、响应分析研究,近年来主要关注数据驱动与不确定性量化方法等. 发表学术论文10余篇,受理或授权专利5项.|孟松鹤, 1969年出生, 哈尔滨工业大学航天学院院长,复合材料与结构研究所暨特种环境复合材料技术国家级重点实验室教授、博士生导师,长江学者特聘教授, 中国复合材料学会常务理事.长期从事高温复合材料与环境耦合模拟、高温性能测试与表征、结构响应获取与分析等方面研究工作,在超高温烧蚀防热复合材料模拟表征、非烧蚀防热复合材料强韧化、大尺寸晶体和薄膜材料生长技术以及高温环境在线测试技术等方面取得成果,获国家科技进步二等奖1项、技术发明二等奖2项, 国家自然科学二等奖1项,发表重要学术论文60余篇, 授权发明专利40余项.

通讯作者:
孟松鹤

中图分类号: N39,O3
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出版历程
- 收稿日期: 2018-09-05
- 刊出日期: 2020-10-08

Big Data in mechanical research: Potentials, applications and challenges

¹ Harbin Institute of Technology, National Key Laboratory of Science and Technology for National Defense on Advanced Composites in Special Environments, Harbin 150001, China
² Harbin Institute of Technology, Postdoctoral Research Center for Material Science and Engineering, Harbin 150001, China
³ Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China

More Information

Corresponding author: MENG Songhe

摘要

摘要: 大数据在全世界发展迅猛, 应用成效显著.大数据独特的思维和方法, 为科学研究与探索提供了全新的范式.力学研究中,高时空分辨率、多参数同步观测与高精度、大规模模拟手段的发展,为力学大数据的发展提供了契机,大数据、机器智能方法的应用正呈现快速上升趋势.本文旨在分析大数据思维方法在力学研究中的应用, 及其启示与挑战.首先从大数据资源、大数据科学及大数据技术3个层面分析了大数据的内涵及研究态势,概括了国内外政府及组织机构的大数据发展规划.而后对比分析了力学思维方法与大数据思维方法的特点,指出两者的本质区别在于数据使用方式的不同而带来的范式差异:大数据采用数据驱动模型替代力学中的偏微分方程组以描述问题,在复杂系统的分析、预测中优势显著.回顾了大数据方法在材料性能预测、材料本构建模、湍流建模、结构健康监测及试验力学等方面的最新研究进展,以及动态数据驱动与数字孪生等大数据驱动的建模模拟新范式.总结了大数据在力学研究中应用的3种方式, 即驱动已有模型改进,挖掘复杂隐含的规律, 以及替代已有的理论方法等. 最后,建议以力学研究为主体和牵引, 大数据与力学双驱动,推动大数据与力学交叉形成理论与方法突破、及学科发展新方向.
- 力学 /
- 大数据 /
- 数据科学 /
- 复杂性 /
- 不确定性
Abstract: Big Data has developed rapidly and made remarkable achievements. The unique thinking and methodology of Big Data provide a new paradigm for scientific research. High spatial-temporal resolution and multiple-parameter synchronous observation methods are offering opportunities for Big Data driven mechanics. Applications of Big Data or machine intelligence methods in mechanical researches have grown rapidly. This review is focused on the impact of Big Data method and its way of thinking on mechanical research and the corresponding challenges. First, the connotation and research situation of Big Data in three aspects, i.e. Big Data itself, Big Data science and Big Data technologies are discussed, and Big Data development plans of governments and organizations are summarized. Comparative analysis of the mechanical methodology and the Big Data methodology were carried out, with focuses on paradigm differences in utilization of data. Instead of partial differential equations used by mechanics, data driven models are used for mathematical descriptions of the underlying physical problem in the Big Data paradigm. The latter shows advantages in simulations and predictions of complex systems. Latest researches in material performance prediction, constitutive modeling, turbulence modeling, structural health monitoring, and experimental mechanics using Big Data, as well as Big Data driven new paradigm of modeling and simulation including Dynamic Data Driven Application System and Digital Twin are reviewed. Three kinds of Big Data driven mechanical researches are summarized, i.e. data driven improvement of existing methods, data mining for hidden and intrinsic physical laws, and data based new methodologies and theories for mechanics. A mechanics-centric approach employing both the prior knowledge of mechanics and advantages of Big Data methodology is recommended for a vision of breakthroughs along with new subject directions in mechanics.
- mechanics /
- Big Data /
- data science /
- complexity /
- uncertainty

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