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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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MATHICSE Technical Report : Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Discarding low quality Minutia Cylinder-Code pairs for improved fingerprint comparison

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

MATHICSE Technical Report : Convergence of quasi-optimal stochastic Galerkin methods for a class of PDES with random coefficients

Compressibility of Deterministic and Random Infinite Sequences

Neural Pre-coding Increases the Pattern Retrieval Capacity of Hopﬁeld and Bidirectional Associative Memories

Applications of Derandomization Theory in Coding

Authentication with built-in encryption by using moire parallax effects between fixed correlated s-random layers

Interleaving bisection stimuli - randomly or in sequence - does not disrupt perceptual learning, it just makes it more difficult

Halton Sampling for Image Registration Based on Mutual Information

MATHICSE Technical Report : Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Discarding low quality Minutia Cylinder-Code pairs for improved fingerprint comparison

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

MATHICSE Technical Report : Convergence of quasi-optimal stochastic Galerkin methods for a class of PDES with random coefficients

Compressibility of Deterministic and Random Infinite Sequences

Neural Pre-coding Increases the Pattern Retrieval Capacity of Hopﬁeld and Bidirectional Associative Memories

Applications of Derandomization Theory in Coding

Authentication with built-in encryption by using moire parallax effects between fixed correlated s-random layers

Interleaving bisection stimuli - randomly or in sequence - does not disrupt perceptual learning, it just makes it more difficult

Halton Sampling for Image Registration Based on Mutual Information