Title:

Stochastic PDEs beyond standard monotonicity : well posedness and regularity of solutions

Nonlinear stochastic partial differential equations (SPDEs) are used to model wide variety of phenomena in physics, engineering, finance and economics. In many such models the equations exhibit superlinear growth. In general, equations with superlinear growth are illposed. However if the growth satisfies some monotonicitylike conditions, then wellposedness can be shown. This thesis focuses on SPDEs that satisfy monotonicitylike conditions and consists of two main parts. In part one, we have generalised the results using localmonotonicity condition by establishing the existence and uniqueness of solution to nonlinear stochastic partial differential equations (SPDEs) when the coefficients satisfy local monotonicity condition. This is done by identifying appropriate coercivity condition which helps in obtaining the desired higher order moment estimates without explicitly restricting the growth of the operators acting on the solution in the stochastic integral terms. As a result, we can solve various semilinear and quasilinear stochastic partial differential equations with locally monotone operators, where derivatives may appear in the operator acting on the solution under the stochastic integral term. Examples of such equations are stochastic reactiondiffusion equations, stochastic Burger equations and stochastic pLaplace equations where the diffusion operator need not necessarily be Lipschitz continuous. Further, the operator appearing in bounded variation term is allowed to be the sum of finitely many operators, each having different analytic and growth properties. As an application, wellposedness of the stochastic anisotropic pLaplace equation driven by Levy noise has been shown. In second part of this thesis, new regularity results for solution to semilinear SPDEs on bounded domains are obtained. The semilinear term is continuous, monotone except around the origin and is allowed to have polynomial growth of arbitrary high order. Typical examples are the stochastic AllenCahn and GinzburgLandau equations. This is done by obtaining some Lp estimates which are subsequently employed in obtaining higher regularity of solutions. This is motivated by ongoing work to obtain rate of convergence estimates for numerical approximations to such equations.
