Abstract
Large deviation principles for stochastic evolution equations
In this talk, I will discuss new results on large deviations for stochastic evolution equations.
Starting with the variational setting, I will present a large deviation principle (LDP) for a class of SPDEs that includes many new examples with gradient noise and unbounded spatial domains.
We will then move to non-variational settings and explore LDPs for the stochastic 3D primitive equations and reaction-diffusion equations.
While our method is based on the well-known weak convergence approach, the main novel ingredients come from the theory of critical spaces and maximal regularity techniques.
Based on joint work with Antonio Agresti and Mark Veraar.