Abstract:We consider the approximation via modulation equations for nonlinear SPDEs on unbounded domains with additive space time white noise. Close to a bifurcation an infinite band of eigenvalues changes stability, and we study the impact of small space-time white noise on this bifurcation.As a first example we study the stochastic Swift-Hohenberg equation on the whole real line. Here due to the weak regularity of solutions the standard methods for modulation equations fail, and we need to develop new tools to treat … Show more
“…Moreover, it would be interesting to extend the present considerations to SPDEs on unbounded domains that are intensely studied over the past few years, cf. [4,11]. Such studies are currently in progress and will be reported in future publications.…”
Section: Conclusion and Future Challengesmentioning
confidence: 85%
“…Nevertheless, we will not focus on this case here. See [11] for the full approximation of Swift-Hohenberg perturbed by space-time white noise on the whole real line, [21] in the case of a simple one-dimensional noise, and [12] for large domain.…”
In the present work, we establish the approximation of nonlinear stochastic partial differential equation (SPDE) driven by cylindrical α-stable Lévy processes via modulation or amplitude equations.We study SPDEs with a cubic nonlinearity, where the deterministic equation is close to a change of stability of the trivial solution. The natural separation of time-scales close to this bifurcation allows us to obtain an amplitude equation describing the essential dynamics of the bifurcating pattern, thus reducing the original infinite dimensional dynamics to a simpler finite-dimensional effective dynamics. In the presence of a multiplicative stable Lévy noise that preserves the constant trivial solution we study the impact of noise on the approximation.In contrast to Gaussian noise, where non-dominant pattern are uniformly small in time due to averaging effects, large jumps in the Lévy noise might lead to large error terms, and thus new estimates are needed to take this into account.
“…Moreover, it would be interesting to extend the present considerations to SPDEs on unbounded domains that are intensely studied over the past few years, cf. [4,11]. Such studies are currently in progress and will be reported in future publications.…”
Section: Conclusion and Future Challengesmentioning
confidence: 85%
“…Nevertheless, we will not focus on this case here. See [11] for the full approximation of Swift-Hohenberg perturbed by space-time white noise on the whole real line, [21] in the case of a simple one-dimensional noise, and [12] for large domain.…”
In the present work, we establish the approximation of nonlinear stochastic partial differential equation (SPDE) driven by cylindrical α-stable Lévy processes via modulation or amplitude equations.We study SPDEs with a cubic nonlinearity, where the deterministic equation is close to a change of stability of the trivial solution. The natural separation of time-scales close to this bifurcation allows us to obtain an amplitude equation describing the essential dynamics of the bifurcating pattern, thus reducing the original infinite dimensional dynamics to a simpler finite-dimensional effective dynamics. In the presence of a multiplicative stable Lévy noise that preserves the constant trivial solution we study the impact of noise on the approximation.In contrast to Gaussian noise, where non-dominant pattern are uniformly small in time due to averaging effects, large jumps in the Lévy noise might lead to large error terms, and thus new estimates are needed to take this into account.
“…In [7] the authors study large domains, where the dominating pattern is slowly modulated in space and the amplitude equation is thus an SPDE of Ginzburg-Landau type. See also [2] for an example of a fully unbounded domain.…”
Section: Introductionmentioning
confidence: 99%
“…Numerous extensions of our results are imaginable. While including quadratic or higher order nonlinearities seem to be technical but possible, unbounded or large domains as in [2] are a more challenging question. Moreover, it would be desirable to extend these results to SPDEs perturbed by rough nonlinear multiplicative noise.…”
We study stochastic partial differential equations (SPDEs) with potentially very rough fractional noise with Hurst parameter H ∈ (0, 1). Close to a change of stability measured with a small parameter ε, we rely on the natural separation of time-scales and establish a simplified description of the essential dynamics. We prove that up to an error term bounded by a power of ε depending on the Hurst parameter we can approximate the solution of the SPDE in first order by an SDE, the so called amplitude equation, and in second order by a fast infinite dimensional Ornstein-Uhlenbeck process. To this aim we need to establish an explicit averaging result for stochastic integrals driven by rough fractional noise for small Hurst parameters.
“…For the case of SPDEs on unbounded domains the effective equation is no longer an SDE, but the reduced model is still given as an infinite dimensional SPDE. For details see [22] in the case of a simple one-dimensional noise, [7] for large domains and [2,19] for results with space-time white noise and on an unbounded domain. Here we will focus on the case of bounded domains only.…”
This article deals with the approximation of a stochastic partial differential equation (SPDE) via amplitude equations. We consider an SPDE with a cubic nonlinearity perturbed by a general multiplicative noise that preserves the constant trivial solution and we study the dynamics around it for the deterministic equation being close to a bifurcation.Based on the separation of time-scales close to a change of stability, we rigorously derive an amplitude equation describing the dynamics of the bifurcating pattern.This allows us to approximate the original infinite dimensional dynamics by a simpler effective dynamics associated with the solution of the amplitude equation. To illustrate the abstract result we apply it to a simple onedimensional stochastic Ginzburg-Landau equation.
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