Regression points in non-intrusive polynomial chaos expansion method and D-optimal design

Katarzyna Szepietowska; Benoit Magnain; Izabela Lubowiecka; Eric Florentin

Regression points in non-intrusive polynomial chaos expansion method and D-optimal design

Abstract

The paper addresses selected issues of uncertainty quantification in the modelling of a system containing surgical mesh used in ventral hernia repair. Uncertainties in the models occur e.g. due to variability of abdominal wall properties among others. In order to include them, a non-intrusive regression-based polynomial chaos expansion method is employed. Its accuracy depends on the choice of regression points. In the study a relation between error of mean, standard deviation, 95th percentile and location of regression points is investigated on the models of implants with a single random variable. This approach is compared with a classic choice of points based on the D-optimality criterion.

Authors (4)

Katarzyna Szepietowska dr inż.
Benoit Magnain dr
- INSA Centre Val de Loire / Univ. Tours / Univ. Orléans LAME
Izabela Lubowiecka dr hab. inż.
Eric Florentin prof.
- INSA Centre Val de Loire / Univ. Tours / Univ. Orléans LAME

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Category:

Articles

Type:

artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych

Published in:

Machine Dynamics Research no. 41, pages 5 - 16,
ISSN: 2080-9948

Language:

English

Publication year:

2017

Bibliographic description:

Szepietowska K., Magnain B., Lubowiecka I., Florentin E.: Regression points in non-intrusive polynomial chaos expansion method and D-optimal design// Machine Dynamics Research. -Vol. 41., iss. 2 (2017), s.5-16

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