一种非参数概率方法,通过数据驱动方法增强PGD解决方案,应用于自动化胶带放置过程

Ghnatios, Chady, Barasinski, Anais
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引用次数: 2

摘要

一种非参数方法评估误差和可变性的边界在一个分离的形式描述的解决方案,使用实验结果说明了这一工作。当实验结果可用时,该方法评估了解决方案的总变异性,包括建模误差和截断误差。所示的方法是基于使用分离形式的PGD解,并通过将部分PGD基向量转换为概率基向量来丰富。所构造的概率向量被限制在物理解的Stiefel流形中。结果是一个实时的参数PGD解增强了解的可变性和置信区间。
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A nonparametric probabilistic method to enhance PGD solutions with data-driven approach, application to the automated tape placement process
A nonparametric method assessing the error and variability margins in solutions depicted in a separated form using experimental results is illustrated in this work. The method assess the total variability of the solution including the modeling error and the truncation error when experimental results are available. The illustrated method is based on the use of the PGD separated form solutions, enriched by transforming a part of the PGD basis vectors into probabilistic one. The constructed probabilistic vectors are restricted to the physical solution’s Stiefel manifold. The result is a real-time parametric PGD solution enhanced with the solution variability and the confidence intervals.
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来源期刊
Advanced Modeling and Simulation in Engineering Sciences
Advanced Modeling and Simulation in Engineering Sciences Engineering-Engineering (miscellaneous)
CiteScore
6.80
自引率
0.00%
发文量
22
审稿时长
30 weeks
期刊介绍: The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which are treated in this journal.
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