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20102026
most citedThe aggregated unfitted finite element method for elliptic problems

152 citations

Showing 2021 · math.NAShow all

6 papers · 2 filters

math.NA2021★ 15 cited

Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information

Matteo Giacomini, Simona Perotto

A finite element-based image segmentation strategy enhanced by an anisotropic mesh adaptation procedure is presented. The methodology relies on a split Bregman algorithm for the mi…

math.NA2021★ 23 cited

Geometrical discretisations for unfitted finite elements on explicit boundary representations

Santiago Badia, Pere A. Martorell, Francesc Verdugo

Unfitted (also known as embedded or immersed) finite element approximations of partial differential equations are very attractive because they have much lower geometrical requireme…

math.NA2021★ 34 cited

High performance reduction technique for multiscale finite element modeling (HPR-FE2): towards industrial multiscale FE software

Marcelo Raschi, Oriol Lloberas-Valls, Alfredo Huespe +1

The authors have shown in previous contributions that reduced order modeling with optimal cubature applied to finite element square (FE2) techniques results in a reliable and affor…

math.NA2021★ 1 cited

A posteriori goal-oriented bounds for the Poisson problem using potential and equilibrated flux reconstructions: application to the hybridizable discontinuous Galerkin method

Nuria Pares, Ngoc-Cuong Nguyen, Pedro Diez +1

We present a general framework to compute upper and lower bounds for linear-functional outputs of the exact solutions of the Poisson equation based on reconstructions of the field…

math.NA2021★ 47 cited

Linking ghost penalty and aggregated unfitted methods

Santiago Badia, Eric Neiva, Francesc Verdugo

In this work, we analyse the links between ghost penalty stabilisation and aggregation-based discrete extension operators for the numerical approximation of elliptic partial differ…

math.NA2021

Nonlinear dimensionality reduction for parametric problems: a kernel Proper Orthogonal Decomposition (kPOD)

Pedro Díez, Alba Muixí, Sergio Zlotnik +1

Reduced-order models are essential tools to deal with parametric problems in the context of optimization, uncertainty quantification, or control and inverse problems. The set of pa…