Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods.

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Science and Engineering Pub Date : 2022-01-01 Epub Date: 2022-08-21 DOI:10.1007/s41019-022-00193-5
Chun Kit Jeffery Hou, Kamran Behdinan
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引用次数: 12

Abstract

Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and computer-assisted engineering. The modeling demand exponentially increases with complexity and number of system parameters, which consequently requires higher-dimensional engineering solving techniques. This is known as the curse of dimensionality. Surrogate models are commonly used to replace costly computational simulations and modeling of complex geometries. However, an ongoing challenge is to reduce execution and memory consumption of high-complexity processes, which often exhibit nonlinear phenomena. Dimensionality reduction algorithms have been employed for feature extraction, selection, and elimination for simplifying surrogate models of high-dimensional problems. By applying dimensionality reduction to surrogate models, less computation is required to generate surrogate model parts while retaining sufficient representation accuracy of the full process. This paper aims to review the current literature on dimensionality reduction integrated with surrogate modeling methods. A review of the current state-of-the-art dimensionality reduction and surrogate modeling methods is introduced with a discussion of their mathematical implications, applications, and limitations. Finally, current studies that combine the two topics are discussed and avenues of further research are presented.

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代理模型的降维:综合方法综述。
在复杂的工程过程中,如制造和计算机辅助工程中,代理模型作为全尺寸模型的替代方法已经得到了推广。建模需求随着系统参数的复杂性和数量呈指数增长,因此需要更高维度的工程求解技术。这就是众所周知的维度诅咒。代理模型通常用于取代昂贵的计算模拟和复杂几何形状的建模。然而,一个持续的挑战是减少高复杂性进程的执行和内存消耗,这些进程经常表现出非线性现象。降维算法已被用于特征提取、选择和消除,以简化高维问题的代理模型。通过对代理模型应用降维,生成代理模型部件所需的计算更少,同时保持整个过程的足够表示精度。本文旨在综述目前关于降维与代理建模方法集成的文献。对当前最先进的降维和代理建模方法进行了回顾,并讨论了它们的数学含义、应用和局限性。最后,讨论了结合这两个主题的当前研究,并提出了进一步研究的途径。
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来源期刊
Data Science and Engineering
Data Science and Engineering Engineering-Computational Mechanics
CiteScore
10.40
自引率
2.40%
发文量
26
审稿时长
12 weeks
期刊介绍: The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data,(f)  storage, transmission, and management of big data,(g) methods and algorithms of  data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and  big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.
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