Weak convergence for the stochastic heat equation driven by Gaussian white noise

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Abstract

In this paper, we consider a quasi-linear stochastic heat equation on [0, 1], with Dirichlet boundary conditions and controlled by the space-time white noise. We formally replace the random perturbation by a family of noisy inputs depending on a parameter n ∈ ℕ that approximate the white noise in some sense. Then, we provide sufficient conditions ensuring that the realvalued mild solution of the SPDE perturbed by this family of noises converges in law, in the space C([0, T]×[0, 1]) of continuous functions, to the solution of the white noise driven SPDE. Making use of a suitable continuous functional of the stochastic convolution term, we show that it suffices to tackle the linear problem. For this, we prove that the corresponding family of laws is tight and we identify the limit law by showing the convergence of the finite dimensional distributions. We have also considered two particular families of noises to that our result applies. The first one involves a Poisson process in the plane and has been motivated by a one-dimensional result of Stroock, which states that the family of processes n ∫t0(-1)N(n2s)ds, where N is a standard Poisson process, converges in law to a Brownian motion. The second one is constructed in terms of the kernels associated to the extension of Donsker’s theorem to the plane. © 2010 Applied Probability Trust.
Original languageEnglish
Pages (from-to)1267-1295
JournalElectronic Journal of Probability
Volume15
DOIs
Publication statusPublished - 1 Jan 2010

Keywords

  • Donsker kernels
  • Stochastic heat equation
  • Two-parameter Poisson process
  • Weak convergence
  • White noise

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