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.

中文翻译：

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一种使用数据驱动方法增强 PGD 解决方案的非参数概率方法，应用于自动磁带放置过程

这项工作说明了一种非参数方法，该方法使用实验结果评估以分离形式描述的解决方案中的误差和可变性裕度。当实验结果可用时，该方法评估解决方案的总可变性，包括建模误差和截断误差。所示方法基于使用 PGD 分离形式的解决方案，通过将一部分 PGD 基础向量转换为概率基础向量来丰富。构造的概率向量仅限于物理解的 Stiefel 流形。结果是实时参数 PGD 解决方案增强了解决方案的可变性和置信区间。