Multilevel Monte Carlo Methods with Smoothing (Anastasia Istratuca, University of Edinburgh)

19.11.2024 14:00

We consider the computational efficiency of Monte Carlo (MC) and Multilevel Monte Carlo (MLMC) methods applied to elliptic partial differential equations with random coefficients. We make use of the circulant embedding procedure to sample from the aforementioned coefficient. Then, to further improve the computational complexity of the MLMC estimator, we devise and implement the smoothing technique integrated into the circulant embedding method. This allows to choose the coarsest mesh on the first level of MLMC independently of the correlation length of the covariance function of the random field, leading to considerable savings in computational cost.

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Bâtiment: Conseil Général 7-9

Room 1-05, Séminaire d'analyse numérique

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