A spectral-based numerical method for Kolmogorov equations in Hilbert spaces
For researchers solving stochastic PDEs, this offers a new numerical approach, but the lack of quantitative results makes its practical impact unclear.
The paper proposes a spectral-based numerical method for solving Kolmogorov equations in Hilbert spaces, reformulating them as a truncated system of ODEs via Wiener-Chaos expansion. Tests on three stochastic PDEs show the method's applicability, but no concrete numerical results or comparisons are provided.
We propose a numerical solution for the solution of the Fokker-Planck-Kolmogorov (FPK) equations associated with stochastic partial differential equations in Hilbert spaces. The method is based on the spectral decomposition of the Ornstein-Uhlenbeck semigroup associated to the Kolmogorov equation. This allows us to write the solution of the Kolmogorov equation as a deterministic version of the Wiener-Chaos Expansion. By using this expansion we reformulate the Kolmogorov equation as a infinite system of ordinary differential equations, and by truncation it we set a linear finite system of differential equations. The solution of such system allow us to build an approximation to the solution of the Kolmogorov equations. We test the numerical method with the Kolmogorov equations associated with a stochastic diffusion equation, a Fisher-KPP stochastic equation and a stochastic Burgers Eq. in dimension 1.