高斯过程模型的非均匀主动学习及其在轨迹信息空气动力学数据库中的应用

IF 2.1 4区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kevin R. Quinlan, Jagadeesh Movva, Brad Perfect
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引用次数: 0

摘要

对许多应用来说,对输入空间进行非均匀加权的能力是可取的,并已在空间填充方法中进行了探索。随着人们对数字孪生框架等模型链接兴趣的增加,在最有可能进行评估的地方对模拟器进行采样的需求也随之增加。特别是,我们将非均匀采样方法应用于空气动力学数据库的构建。本文将非均匀加权与高斯过程(GPs)的主动学习相结合,为非均匀主动学习准则开发了一种闭式解决方案。我们利用核密度估计器作为权重函数来实现这一目标。我们通过一个大气进入示例来证明这种方法的必要性和有效性,该示例既考虑了模型的不确定性,也考虑了车辆的实际状态空间,这是由主动学习环路中的前向建模决定的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non‐uniform active learning for Gaussian process models with applications to trajectory informed aerodynamic databases
The ability to non‐uniformly weight the input space is desirable for many applications, and has been explored for space‐filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evaluated. In particular, we apply non‐uniform sampling methods for the construction of aerodynamic databases. This paper combines non‐uniform weighting with active learning for Gaussian Processes (GPs) to develop a closed‐form solution to a non‐uniform active learning criterion. We accomplish this by utilizing a kernel density estimator as the weight function. We demonstrate the need and efficacy of this approach with an atmospheric entry example that accounts for both model uncertainty as well as the practical state space of the vehicle, as determined by forward modeling within the active learning loop.
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来源期刊
Statistical Analysis and Data Mining
Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
3.20
自引率
7.70%
发文量
43
期刊介绍: Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.
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