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Monte Carlo Methods and Applications

Managing Editor: Sabelfeld, Karl K.

Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis

4 Issues per year


CiteScore 2016: 0.70

SCImago Journal Rank (SJR) 2016: 0.647
Source Normalized Impact per Paper (SNIP) 2016: 0.908

Mathematical Citation Quotient (MCQ) 2016: 0.33

Online
ISSN
1569-3961
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Volume 10, Issue 3-4

Issues

Adaptive adjoint Monte Carlo simulation for the uncertainty

M. Magolu monga Made / O. F. Smidts / A. Dubus
Published Online: 2008-05-09 | DOI: https://doi.org/10.1515/mcma.2004.10.3-4.403

In order to take into account uncertainties about the values of the hydro-geological parameters of the rock hosting a deep geological repository, probabilistic methods are used in the risk assessment of radioactive waste repositories. Random generators could be globally invoked twice in adjoint Monte Carlo (AMC) simulation. Once for sampling hydro-geological parameters from known probability density functions (pdf). Next, for each selected set of parameters, random walks could be simulated for the evaluation of concentration of contaminants. With a moderate number of random walks (batch size), AMC method is efficient for computing mean values of concentrations. However, the higher moments of the concentration distribution and the distribution tails are in general not evaluated with accuracy. To cope with these inconveniences, we propose an adaptive AMC method in which the batch size is dynamically increased. The new approach is applied for the accurate assessment of the probability of exceeding some imposed critical concentrations.

Keywords: Migration of radionuclides; transport model; partial differential equations; integral formulation; Adjoint Monte Carlo method; probability distribution function; statistical uncertainty

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Published Online: 2008-05-09

Published in Print: 2004-12-01


Citation Information: Monte Carlo Methods and Applications mcma, Volume 10, Issue 3-4, Pages 403–413, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/mcma.2004.10.3-4.403.

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