Kriging, Polynomial Chaos Expansion, and Low-Rank Approximations in Material Science and Big Data Analytics.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-08-01 Epub Date: 2023-04-24 DOI:10.1089/big.2022.0124
Golsa Mahdavi, Mohammad Amin Hariri-Ardebili
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引用次数: 0

Abstract

In material science and engineering, the estimation of material properties and their failure modes is associated with physical experiments followed by modeling and optimization. However, proper optimization is challenging and computationally expensive. The main reason is the highly nonlinear behavior of brittle materials such as concrete. In this study, the application of surrogate models to predict the mechanical characteristics of concrete is investigated. Specifically, meta-models such as polynomial chaos expansion, Kriging, and canonical low-rank approximation are used for predicting the compressive strength of two different types of concrete (collected from experimental data in the literature). Various assumptions in surrogate models are examined, and the accuracy of each one is evaluated for the problem at hand. Finally, the optimal solution is provided. This study paves the road for other applications of surrogate models in material science and engineering.

材料科学和大数据分析中的克里金法、多项式混沌展开和低域近似。
在材料科学与工程领域,材料特性及其失效模式的估算与物理实验有关,随后是建模和优化。然而,适当的优化具有挑战性,且计算成本高昂。主要原因在于混凝土等脆性材料的高度非线性行为。在本研究中,将研究如何应用代用模型来预测混凝土的力学特性。具体来说,多项式混沌扩展、克里金法和典型低阶近似等元模型被用于预测两种不同类型混凝土的抗压强度(从文献中收集的实验数据)。研究了代用模型中的各种假设,并针对当前问题评估了每种假设的准确性。最后,提供了最佳解决方案。这项研究为代用模型在材料科学与工程领域的其他应用铺平了道路。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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