Some approximation results for mild solutions of stochastic fractional order evolution equations driven by Gaussian noise

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Authors

External Organisational units

  • Pázmány Péter Catholic University
  • Budapest University of Technology and Economics
  • Chalmers University of Technology

Abstract

We investigate the quality of space approximation of a class of stochastic integral equations of convolution type with Gaussian noise. Such equations arise, for example, when considering mild solutions of stochastic fractional order partial differential equations but also when considering mild solutions of classical stochastic partial differential equations. The key requirement for the equations is a smoothing property of the deterministic evolution operator which is typical in parabolic type problems. We show that if one has access to nonsmooth data estimates for the deterministic error operator together with its derivative of a space discretization procedure, then one obtains error estimates in pathwise Hölder norms with rates that can be read off the deterministic error rates. We illustrate the main result by considering a class of stochastic fractional order partial differential equations and space approximations performed by spectral Galerkin methods and finite elements. We also improve an existing result on the stochastic heat equation.

Details

Original languageEnglish
Article number11
Pages (from-to)1044-1088
Number of pages45
JournalStochastics and Partial Differential Equations: Analysis and Computations
Volume2022
Issue number11
Early online date26 Apr 2022
DOIs
Publication statusPublished - 2023