Exponentially accurate spectral Monte Carlo method for linear PDEs and their error estimates

This paper introduces a spectral Monte Carlo iterative method (SMC) for solving linear Poisson and parabolic equations driven by a-stable L & eacute;vy processes with a is an element of (0, 2), which was initially proposed and developed by Gobet and Maire in their pioneering works ((2004) [24], and (2005) [25]) for the case a = 2. The novel method effectively integrates multiple computational techniques, including the interpolation based on generalized Jacobi functions (GJFs), space-time spectral methods, control variates techniques, and a novel walk-on-spheres method (WOS). The exponential convergence of the error bounds is rigorously established through finite iterations for both Poisson and parabolic equations involving the integral fractional Laplacian operator. Remarkably, the proposed space-time spectral Monte Carlo method (ST-SMC) for the parabolic equation is unified for both a is an element of (0,2) and a = 2. Extensive numerical results are provided to demonstrate the spectral accuracy and efficiency of the proposed method, thereby validating the theoretical findings.