Well-posedness and uniform large deviation principle for stochastic generalised Burgers-Huxley equation perturbed by a multiplicative noise
A talk in the SPDEvent series by
Ankit Kumar from Montanuniversität Leoben
| Abstract: | In this talk, we focus on the global solvability and uniform large deviations for the solutions of stochastic generalised Burgers-Huxley (SGBH) equation perturbed by a small multiplicative white in time and coloured in space noise. The SGBH equation has the nonlinearity of polynomial order and noise considered in this work is infinite-dimensional with a coefficient having linear growth. First, we prove the existence of a unique local mild solution in the sense of Walsh to SGBH equation with the help of a truncation argument and contraction mapping principle. Then the global solvability results are established by using uniform bounds of the local mild solution, stopping time arguments, tightness properties and Skorokhod’s representation theorem. By using the uniform Laplace principle, we obtain the large deviation principle (LDP) for the law of solutions to SGBH equation by
using variational representation methods. Further, we derive the uniform large deviation principle (ULDP) for the law of solutions in two different topologies by using a weak convergence method. First, in the $C([0, T]; L^p([0, 1]))$ topology where the uniformity is over $L^p([0, 1])$-bounded sets of initial conditions, and secondly in the $C([0, T] \times [0, 1])$ topology with uniformity being over bounded subsets in the $C([0, 1])$. This talk is based on joint work with Vivek Kumar (ISI Bangalore) and Manil T. Mohan (IIT Roorkee). |