Fabio Nobile
École Polytechnique Fédérale de Lausanne
213 Papers
715 Citations
Fabio Nobile is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Monte Carlo method & Random field. The author has an hindex of 44, co-authored 198 publications. Previous affiliations of Fabio Nobile include Polytechnic University of Milan & École Normale Supérieure.
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Papers
A posteriori error estimation for the steady Navier-Stokes equations in random domains
TL;DR: In this paper, the authors consider finite element error approximations of the steady incompressible Navier-Stokes equations defined on a randomly perturbed domain, the perturbation being small.
Partitioned Algorithms for Fluid-Structure Interaction Problems in Haemodynamics
Fabio Nobile,Christian Vergara +1 more
TL;DR: In this article, the fluid-structure interaction problem arising in haemodynamic applications is considered, where finite elasticity equations for the vessel are written in Lagrangian form, while the Navier-Stokes equation for the blood in Arbitrary Lagrangians Eulerian form.
Analytic regularity and collocation approximation for elliptic PDEs with Random domain deformations
TL;DR: It is shown that the solution of a linear elliptic PDE defined over a random domain parameterized by N random variables can be analytically extended to a well defined region in C^N with respect to the random variables.
MATHICSE Technical Report: Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval
Yoshihito Kazashi,Fabio Nobile,MATHICSE-Group +2 more
- 06 Feb 2020
TL;DR: An existence result is presented for the dynamical low rank (DLR) approximation for random semi-linear evolutionary equations, based on an abstract Cauchy problem in a suitable linear space, for which existence and uniqueness of the solution in the maximal interval are established.
A Multilevel Monte Carlo Evolutionary Algorithm for Robust Aerodynamic Shape Design
Michele Pisaroni,Fabio Nobile,Pénélope Leyland +2 more
- 05 Jun 2017
TL;DR: A novel approach for robust optimization of aerodynamic shapes based on the combination of single and multi-objective Evolutionary Algorithms and a Continuation Multi Level Monte Carlo methodology to estimate robust designs, without relying on derivatives and meta-models is presented.