Regression points in non-intrusive polynomial chaos expansion method and d-optimal design - Publikacja - MOST Wiedzy

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

Abstrakt

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 due to variability of abdominal wall properties among others. In order to include them, a nonintrusive 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 in 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.

Katarzyna Szepietowska, Benoit Magnain, Izabela Lubowiecka, Eric Florentin. (2018). Regression points in non-intrusive polynomial chaos expansion method and d-optimal design, 41(2), 4-16.

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
Opublikowano w:
Machine Dynamics Research nr 41, strony 4 - 16,
ISSN: 2080-9948
Język:
angielski
Rok wydania:
2018
Opis bibliograficzny:
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., nr. 2 (2018), s.4-16

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