S
Simone Deparis
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 74
Citations - 2087
Simone Deparis is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Finite element method & Discretization. The author has an hindex of 22, co-authored 69 publications receiving 1795 citations. Previous affiliations of Simone Deparis include International Association of Classification Societies & École Polytechnique.
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Fluid―structure interaction simulation of aortic blood flow
Paolo Crosetto,Philippe Reymond,Simone Deparis,Dimitrios Kontaxakis,Nikolaos Stergiopulos,Alfio Quarteroni,Alfio Quarteroni +6 more
TL;DR: The present work aims to address and validate new algorithms to efficiently predict the hemodynamics in large arteries using finite elements simulation of the fluid–structure interaction between blood flow and arterial wall deformation of a healthy aorta.
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Fluid–structure algorithms based on Steklov–Poincaré operators
TL;DR: In this paper, an alternative viewpoint mutuated from the domain decomposition theory is proposed, which yields preconditioned Richardson iterations on the Steklov-Poincare nonlinear equation at the fluid-structure interface.
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Physiological simulation of blood flow in the aorta: comparison of hemodynamic indices as predicted by 3-D FSI, 3-D rigid wall and 1-D models.
Philippe Reymond,Paolo Crosetto,Simone Deparis,Alfio Quarteroni,Alfio Quarteroni,Nikolaos Stergiopulos +5 more
TL;DR: 3-D fluid-structure interaction is used to provide physiological simulation resulting in modeling with a high level of detail and a comparison of the 3-D simulations to the 1-D model shows good reproduction of the pressure and flow waveforms.
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Parallel Algorithms for Fluid-Structure Interaction Problems in Haemodynamics
TL;DR: This work introduces a class of parallel preconditioners for the FSI problem obtained by exploiting the block-structure of the linear system and shows that the construction and evaluation of the devised preconditionser is modular.
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Natural norm a posteriori error estimators for reduced basis approximations
TL;DR: A new "natural norm" formulation for the reduced basis error estimation framework is proposed that greatly simplifies and improves the inf-sup lower bound construction (offline) and evaluation (online) and significantly sharpens - the authors' output error bounds.