Skip to Main content Skip to Navigation
Journal articles

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

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.
Complete list of metadata

https://hal-univ-pau.archives-ouvertes.fr/hal-03354962
Contributor : Sylvie Blanc Connect in order to contact the contributor
Submitted on : Monday, September 27, 2021 - 8:04:18 AM
Last modification on : Tuesday, September 28, 2021 - 3:36:35 AM

File

s40323-021-00205-5.pdf
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Collections

Citation

Chady Ghnatios, Anaïs Barasinski. A nonparametric probabilistic method to enhance PGD solutions with data-driven approach, application to the automated tape placement process. Advanced Modeling and Simulation in Engineering Sciences, SpringerOpen, 2021, 8 (1), pp.20. ⟨10.1186/s40323-021-00205-5⟩. ⟨hal-03354962⟩

Share

Metrics

Record views

14

Files downloads

18