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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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Quantization Design for Unconstrained Distributed Optimization

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Assessing robustness regarding weather uncertainties for energy-efficiency-driven building refurbishments

DASS: Distributed Adaptive Sparse Sensing

The waste collection VRP with intermediate facilities, a heterogeneous fixed fleet and a flexible assignment of origin and destination depot

Quantization Design for Unconstrained Distributed Optimization

Morphology, microstructure, crystallography, and chemistry of distinct CaCO3 deposits formed by early recruits of the scleractinian coral Pocillopora damicornis

Are flat-field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale

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

MATHICSE Technical Report : Optimization of mesh hierarchies in multilevel Monte Carlo samplers

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