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Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities
arXiv - CS - Machine Learning Pub Date : 2020-06-30 , DOI: arxiv-2006.16728
Lo\"ic Brevault, Mathieu Balesdent, Ali Hebbal

The design process of complex systems such as new configurations of aircraft or launch vehicles is usually decomposed in different phases which are characterized for instance by the depth of the analyses in terms of number of design variables and fidelity of the physical models. At each phase, the designers have to compose with accurate but computationally intensive models as well as cheap but inaccurate models. Multi-fidelity modeling is a way to merge different fidelity models to provide engineers with accurate results with a limited computational cost. Within the context of multi-fidelity modeling, approaches relying on Gaussian Processes emerge as popular techniques to fuse information between the different fidelity models. The relationship between the fidelity models is a key aspect in multi-fidelity modeling. This paper provides an overview of Gaussian process-based multi-fidelity modeling techniques for variable relationship between the fidelity models (e.g., linearity, non-linearity, variable correlation). Each technique is described within a unified framework and the links between the different techniques are highlighted. All the approaches are numerically compared on a series of analytical test cases and four aerospace related engineering problems in order to assess their benefits and disadvantages with respect to the problem characteristics.

中文翻译:

基于高斯过程的多保真度技术概述,保真度之间具有可变关系

复杂系统(例如飞机或运载火箭的新配置)的设计过程通常分解为不同的阶段,例如在设计变量数量和物理模型保真度方面的分析深度。在每个阶段,设计人员都必须使用准确但计算密集型的模型以及廉价但不准确的模型。多保真建模是一种合并不同保真模型的方法,以有限的计算成本为工程师提供准确的结果。在多保真建模的背景下,依赖高斯过程的方法成为融合不同保真模型之间信息的流行技术。保真度模型之间的关系是多保真度建模中的一个关键方面。本文概述了基于高斯过程的多保真建模技术,用于保真模型之间的变量关系(例如,线性、非线性、变量相关)。每种技术都在一个统一的框架内进行描述,并且突出显示了不同技术之间的联系。所有方法都在一系列分析测试案例和四个与航空航天相关的工程问题上进行了数值比较,以评估它们在问题特征方面的优缺点。
更新日期:2020-07-01
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