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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

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Volume 15, Issue 1


On Monte Carlo algorithms applied to Dirichlet problems for parabolic operators in the setting of time-dependent domains

Kaj Nyström
  • Department of Mathematics and Mathematical Statistics, Umeå University, SE-901 87 Umeå, Sweden. Email:
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Thomas Önskog
  • Department of Mathematics and Mathematical Statistics, Umeå University, SE-901 87 Umeå, Sweden. Email:
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2009-05-14 | DOI: https://doi.org/10.1515/MCMA.2009.002


Dirichlet problems for second order parabolic operators in space-time domains Ω ⊂ ℝn+1 are of paramount importance in analysis, partial differential equations and applied mathematics. These problems can be approached in many different ways using techniques from partial differential equations, potential theory, stochastic differential equations, stopped diffusions and Monte Carlo methods. The performance of any technique depends on the structural assumptions on the operator, the geometry and smoothness properties of the space-time domain Ω, the smoothness of the Dirichlet data and the smoothness of the coefficients of the operator under consideration. In this paper, which mainly is of numerical nature, we attempt to further understand how Monte Carlo methods based on the numerical integration of stochastic differential equations perform when applied to Dirichlet problems for uniformly elliptic second order parabolic operators and how their performance vary as the smoothness of the boundary, Dirichlet data and coefficients change from smooth to non-smooth. Our analysis is set in the genuinely parabolic setting of time-dependent domains, which in itself adds interesting features previously only modestly discussed in the literature. The methods evaluated and discussed include elaborations on the non-adaptive method proposed by Gobet [ESAIM: Probability and Statistics 5: 261–297, 2001] based on approximation by half spaces and exit probabilities and the adaptive method proposed in [Lecture Notes in Computational Science and Engineering 44: 59–88, 2005] for weak approximation of stochastic differential equations.

Keywords.: Time-dependent domain; non-smooth domain; heat equation; parabolic partial differential equations; Cauchy–Dirichlet problem; stochastic differential equations; stopped diffusion; Euler scheme; adaptive methods

About the article

Received: 2008-10-10

Revised: 2009-02-09

Published Online: 2009-05-14

Published in Print: 2009-05-01

Citation Information: Monte Carlo Methods and Applications, Volume 15, Issue 1, Pages 11–47, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/MCMA.2009.002.

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