In simulation-based realization of complex systems, we are forced to address the issue of computational complexity. One critical issue that must be addressed is the approximation of reality using surrogate models to replace expensive simulation models of engineering problems. In this paper, we critically review over 200 papers. We find that a framework for selecting appropriate surrogate modeling methods for a given function with specific requirements has been lacking. Having such a framework for surrogate model users, specifically practitioners in industry, is very important because there is very limited information about the performance of different models before applying them on the problem. Our contribution in this paper is to address this gap by creating practical guidance based on a trade-off among three main drivers, namely, size (how much information is necessary to compute the surrogate model), accuracy (how accurate the surrogate model must be) and computational time (how much time is required for the surrogate modeling process). Using the proposed guidance a huge amount of time is saved by avoiding time-consuming comparisons before selecting the appropriate surrogate model. To make this contribution, we review the state-of-the-art surrogate modeling literature to answer the following three questions: (1) What are the main classes of the design of experiment (DOE) methods, surrogate modeling methods and model-fitting methods based on the requirements of size, computational time, and accuracy? (2) Which surrogate modeling method is suitable based on the critical characteristics of the requirements of size, computational time and accuracy? (3) Which DOE is suitable based on the critical characteristics of the requirements of size, computational time and accuracy? Based on these three characteristics, we find six different qualitative categories for the surrogate models through a critical evaluation of the literature. These categories provide a framework for selecting an efficient surrogate modeling process to assist those who wish to select more appropriate surrogate modeling techniques for a given function. It is also summarized in Table 4 and Figs. 2 , 3 . MARS, response surface models, and kriging are more appropriate for large problems, acquiring less computation time and high accuracy, respectively. Also, Latin Hypercube , fractional factorial designs and D-Optimal designs are appropriate experimental designs. Our contribution is to propose a qualitative evaluation and a mental model which is based on quantitative results and findings of authors in the published literature. The value of such a framework is in providing practical guide for researchers and practitioners in industry to choose the most appropriate surrogate model based on incomplete information about an engineering design problem. Another contribution is to use three drivers, namely, computational time, accuracy, and problem size instead of using a single measure that authors generally use in the published literature.

中文翻译：

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使用代理模型管理计算复杂性：批判性审查

在复杂系统的基于仿真的实现中，我们不得不解决计算复杂性的问题。必须解决的一个关键问题是使用代理模型来替代昂贵的工程问题模拟模型来逼近现实。在本文中，我们批判性地审查了 200 多篇论文。我们发现缺乏为具有特定要求的给定函数选择合适的代理建模方法的框架。为代理模型用户，特别是行业从业者，拥有这样一个框架非常重要，因为在将不同模型应用于问题之前，关于不同模型性能的信息非常有限。我们在本文中的贡献是通过基于三个主要驱动因素之间的权衡创建实用指南来解决这一差距，即：大小（计算代理模型需要多少信息）、准确性（代理模型必须有多准确）和计算时间（代理建模过程需要多少时间）。通过在选择合适的替代模型之前避免耗时的比较，使用建议的指导可以节省大量时间。为了做出这一贡献，我们回顾了最先进的代理建模文献，以回答以下三个问题：(1) 实验设计 (DOE) 方法、代理建模方法和模型拟合的主要类别是什么？基于大小、计算时间和精度要求的方法？(2) 根据尺寸要求的关键特征，适合哪种代理建模方法，计算时间和精度？(3) 根据尺寸、计算时间和精度要求的关键特征，哪种 DOE 适合？基于这三个特征，我们通过对文献的批判性评估为代理模型找到了六个不同的定性类别。这些类别为选择有效的代理建模过程提供了一个框架，以帮助那些希望为给定函数选择更合适的代理建模技术的人。它还总结在表 4 和图 3 中。2、3。MARS、响应面模型和克里金法更适用于大型问题，分别获得较少的计算时间和较高的精度。此外，拉丁超立方体、部分因子设计和 D 最优设计是合适的实验设计。我们的贡献是提出一个定性评估和一个基于定量结果和作者在已发表文献中的发现的心理模型。这种框架的价值在于为工业研究人员和从业人员提供实用指南，以根据工程设计问题的不完整信息选择最合适的替代模型。另一个贡献是使用三个驱动因素，即计算时间、准确性和问题大小，而不是使用作者通常在已发表文献中使用的单一度量。这种框架的价值在于为工业研究人员和从业人员提供实用指南，以根据工程设计问题的不完整信息选择最合适的替代模型。另一个贡献是使用三个驱动因素，即计算时间、准确性和问题大小，而不是使用作者通常在已发表文献中使用的单一度量。这种框架的价值在于为工业研究人员和从业人员提供实用指南，以根据工程设计问题的不完整信息选择最合适的替代模型。另一个贡献是使用三个驱动因素，即计算时间、准确性和问题规模，而不是使用作者通常在已发表文献中使用的单一度量。