Well-posedness and uniform large deviation principle for stochastic Burgers-Huxley equation perturbed by a multiplicative noise

Abstract: In this work, we focus on the global solvability and uniform large deviations for the solutions of stochastic generalized Burgers-Huxley (SGBH) equation perturbed by a small multiplicative white in time and colored 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 \textsl{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 \textsl{large deviation principle} (LDP) for the law of solutions to SGBH equation by using variational representation methods. Further, we derive the \textsl{uniform large deviation principle} (ULDP) for the law of solutions in two different topologies by using a weak convergence method. First, in the $\mathrm{C}([0, T ];\mathrm{L}^p([0,1])) $ topology where the uniformity is over $\mathrm{L}^p([0,1])$-bounded sets of initial conditions, and secondly in the $\mathrm{C} ([0, T ] \times[0,1])$ topology with uniformity being over bounded subsets in the $\mathrm{C}([0,1])$-norm. Finally, we consider SGBH equation perturbed by a space-time white noise with bounded noise coefficient and establish the ULDP for the laws of solutions. The results obtained in this work hold true for stochastic Burgers' as well as Burgers-Huxley equations.