A nonparametric probabilistic method to enhance PGD solutions with data-driven approach, application to the automated tape placement process

Abstract

Abstract 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.

Keywords

• Proper Generalized Decomposition
• Model reduction
• Automated tape placement
• hybrid modeling
• Nonparametric probabilistic method
• Machine learning