F
Fabio Nobile
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 211
Citations - 11410
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 receiving 10181 citations. Previous affiliations of Fabio Nobile include Polytechnic University of Milan & École Normale Supérieure.
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A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
TL;DR: A rigorous convergence analysis is provided and exponential convergence of the “probability error” with respect to the number of Gauss points in each direction in the probability space is demonstrated, under some regularity assumptions on the random input data.
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A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data
TL;DR: This work demonstrates algebraic convergence with respect to the total number of collocation points and quantifies the effect of the dimension of the problem (number of input random variables) in the final estimates, indicating for which problems the sparse grid stochastic collocation method is more efficient than Monte Carlo.
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Added-mass effect in the design of partitioned algorithms for fluid-structure problems
TL;DR: A simplified model representing the interaction between a potential fluid and a linear elastic thin tube is considered, which reproduces propagation phenomena and takes into account the added-mass effect of the fluid on the structure, which is known to be source of numerical difficulties.
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On the coupling of 3D and 1D Navier-Stokes equations for flow problems in compliant vessels
TL;DR: In this article, the authors propose an approach to couple the original 3D equations with a convenient 1D model for the analysis of flows in compliant vessels, which allows for a dramatic reduction of the computational complexity and is suitable for ''absorbing» outgoing pressure waves.
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An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data
TL;DR: This work proposes and analyzes an anisotropic sparse grid stochastic collocation method for solving partial differential equations with random coefficients and forcing terms (input data of the model) and provides a rigorous convergence analysis of the fully discrete problem.