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Publications associées (32)

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Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Fabio Nobile, Lorenzo Tamellini

In this work we consider quasi-optimal versions of the Stochastic Galerkin method for solving linear elliptic PDEs with stochastic coefficients. In particular, we consider the case of a finite number

N

of random inputs and an analytic dependence of the s ...

2014

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Spatial Coupling as a Proof Technique and Three Applications

Exact simulation of max-stable processes

A Multi Level Monte Carlo Method with Control Variate for elliptic PDEs with log-normal coefficients

Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Sampling Curves with Finite Rate of Innovation

MATHICSE Technical Report : A multi level Monte Carlo method with control variate for elliptic PDEs with log-normal coefficients

Inferring the Evolutionary History of Primate microRNA Binding Sites: Overcoming Motif Counting Biases

Perturbation analysis for the Darcy problem with log-normal permeability

A Weighted Reduced Basis Method For Elliptic Partial Differential Equations With Random Input Data

Improving Control of Dexterous Hand Prostheses Using Adaptive Learning

Spatial Coupling as a Proof Technique and Three Applications

Sampling Curves with Finite Rate of Innovation

Exact simulation of max-stable processes

A Multi Level Monte Carlo Method with Control Variate for elliptic PDEs with log-normal coefficients

Perturbation analysis for the Darcy problem with log-normal permeability

Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

A Weighted Reduced Basis Method For Elliptic Partial Differential Equations With Random Input Data

MATHICSE Technical Report : A multi level Monte Carlo method with control variate for elliptic PDEs with log-normal coefficients

Inferring the Evolutionary History of Primate microRNA Binding Sites: Overcoming Motif Counting Biases

Improving Control of Dexterous Hand Prostheses Using Adaptive Learning