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Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

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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

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Graph Chatbot

Chattez avec Graph Search

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MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

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

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Incentive Schemes for Participatory Sensing

Lyapunov exponents of random walks in small random potential: the upper bound

Approaches to Conditional Risk

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

MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

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

Masking vs. Multiparty Computation: How Large Is the Gap for AES?