Stochastic Runge-Kutta Methods for Preserving Maximum Bound Principle of Semilinear Parabolic Equations. Part I: Gaussian Quadrature Rule (CSIAM Transactions on Applied Mathematics, 2024)
In this paper, we propose a class of stochastic Runge-Kutta (SRK) methodsfor solving semilinear parabolic equations. By using the nonlinear Feynman-Kac formula, we first write the solution of the parabolic equation in the form of the backwardstochastic differential equation (BSDE) and then deduce an ordinary differential equation (ODE) containing the conditional expectations with respect to a diffusion process.The time semidiscrete SRK methods are then developed based on the correspondingODE. Under some reasonable constraints on the time step, we theoretically prove themaximum bound principle (MBP) of the proposed methods and obtain their error estimates. By combining the Gaussian quadrature rule for approximating the conditional expectations, we further propose the first- and second-order fully discrete SRKschemes, which can be written in the matrix form. We also rigorously analyze theMBP-preserving and error estimates of the fully discrete schemes. Some numerical experiments are carried out to verify our theoretical results and to show the efficiencyand stability of the proposed schemes.