Stochastic 3D Burgers equations with random initial data

In this talk, we discuss stochastic 3D Burgers equations driven by multiplicative noise, with uncertainty occurring in initial conditions as well. By utilizing Malliavin calculus techniques and randomization method, we establish the global well-posedness result for stochastic Burgers equations with random initial data. Our work can be viewed as an extension of the one-dimensional result in [SIAM J Math Analysis, 2013] to the three-dimensional case.

Based on the joint work with Zhang and Zhou.