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Anker, Felix [Author]; Bayer, Christian [Author]; Eigel, Martin [Author]; Ladkau, Marcel [Author]; Neumann, Johannes [Author]; Schoenmakers, John G.M. [Author]

SDE based regression for random PDEs - [published Version]

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  • Media type: Text; Report; E-Book
  • Title: SDE based regression for random PDEs
  • Contributor: Anker, Felix [Author]; Bayer, Christian [Author]; Eigel, Martin [Author]; Ladkau, Marcel [Author]; Neumann, Johannes [Author]; Schoenmakers, John G.M. [Author]
  • Published: Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2015
  • Issue: published Version
  • Language: English
  • DOI: https://doi.org/10.34657/3124
  • ISSN: 0946-8633; 2198-5855
  • Keywords: stochastic differential equations ; Monte-Carlo ; Partial differential equations with random coefficients ; uncertainty quantification ; stochastic simulation ; Feynman-Kac ; stochastic regression ; Euler-Maruyama ; random PDE
  • Origination:
  • Footnote: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Description: A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
  • Access State: Open Access