Non-intrusive sparse subspace learning for parametrized problems

Domenico Borzacchiello (1) , Jose Aguado (2) , Francisco Chinesta (1)

1 GeM - Institut de Recherche en Génie Civil et Mécanique
2 ICI - Institut de Calcul Intensif

We discuss the use of hierarchical collocation to approximate the numerical solution of parametric models. With respect to traditional projection-based reduced order modeling, the use of a collocation enables non-intrusive approach based on sparse adaptive sampling of the parametric space. This allows to recover the low-dimensional structure of the parametric solution subspace while also learning the functional dependency from the parameters in explicit form. A sparse low-rank approximate tensor representation of the parametric solution can be built through an incremental strategy that only needs to have access to the output of a determin-istic solver. Non-intrusiveness makes this approach straightforwardly applicable to challenging problems characterized by nonlinearity or non affine weak forms. As we show in the various examples presented in the paper, the method can be interfaced with no particular effort to existing third party simulation software making the proposed approach particularly appealing and adapted to practical engineering problems of industrial interest.

Mots clés: Reduced order modeling; Non-intrusiveness; Low rank approximations; Sparse identification; Sparse subspace learning; Hierarchical collocation

Domaines: Génie des procédés; Mécanique [physics.med-ph]; Génie mécanique [physics.class-ph]; Mécanique des structures [physics.class-ph]; Mathématiques [math]

Domenico Borzacchiello, Jose Aguado, Francisco Chinesta. Non-intrusive sparse subspace learning for parametrized problems. Archives of Computational Methods in Engineering, In press, ⟨10.1007/s11831-017-9241-4⟩. ⟨hal-01925360⟩