Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses

Morgane Menz (1, 2) , Christian Gogu (1) , Sylvain Dubreuil (2) , Nathalie Bartoli (2) , Jérôme Morio (2)

1 ICA - Institut Clément Ader
2 DTIS, ONERA, Université de Toulouse [Toulouse]

Running a reliability analysis on engineering problems involving complex numerical models can be computationally very expensive. Hence, advanced methods are required to reduce the number of calls to the expensive computer codes. Adaptive sampling based reliability analysis methods are one promising way to reduce computational costs. Reduced order modelling is another one. In order to further reduce the numerical costs of Kriging based adaptive sampling approaches , the idea developed in this paper consists in coupling both approaches by adaptively deciding whether to use reduced-basis solutions in place of full numerical solutions whenever the performance function needs to be assessed. Thus, a method combining such adaptive sampling based reliability analyses and reduced basis modeling is proposed using on an efficient coupling criterion. The proposed method enabled significant computational cost reductions, while ensuring accurate estimations of failure probabilities.

Mots clés: Reliability analysis; Kriging; Adaptive approaches; Finite elements; Reduced order modeling; Reduced basis; Reduced basis 2010 MSC: 00-01; 99-00

Domaines: Génie mécanique [physics.class-ph]

Morgane Menz, Christian Gogu, Sylvain Dubreuil, Nathalie Bartoli, Jérôme Morio. Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses. 2020. ⟨hal-02405109v2⟩