Large deviations for stochastic evolution equations

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This talk will be broadcast at 13:30 GMT November 27th 2024 on MS Teams only.

Meeting ID: 393 527 089 878
Passcode: dsm7py

Abstract

This talk is about a new large deviation result for solutions to quasilinear SPDEs with small Gaussian noise. We consider a recently developed, general variational framework for stochastic evolution equations. The large deviation principle (LDP) has been successfully proved for such frameworks by means of the so-called weak convergence approach, but often, strong assumptions have to

be made on the coefficients in the equation or on the Gelfand triple. In our current large deviation result, we do not require any additional assumption apart from those required for well-posedness of the equation. This leads to numerous applications for which the LDP was not established yet, in particular equations on unbounded domains with gradient noise. The talk is based on joint work with Mark Veraar.

About Esmée

Esmée is a PhD Student at Delft University of Technology, under the supervision of Prof. Mark Veraar. She is interested in the intersection of (functional) analysis and stochastics, among others in the context of SPDEs.