Problem adapted Hierarchical Model Reduction for the Fokker-Planck equation
This work addresses the computational challenge of solving high-dimensional Fokker-Planck equations by proposing a more adaptive reduction technique, but the improvements over existing methods are not quantified.
The paper introduces a hierarchical model reduction framework for the Fokker-Planck equation that generates a problem-dependent basis in velocity space, reducing dimensionality while preserving solution shape. Numerical experiments show the method's potential, though no concrete performance numbers are reported.
In this paper we introduce a new hierarchical model reduction framework for the Fokker-Planck equation. We reduce the dimension of the equation by a truncated basis expansion in the velocity variable, obtaining a hyperbolic system of equations in space and time. Unlike former methods like the Legendre moment models, the new framework generates a suitable problem-dependent basis of the reduced velocity space that mimics the shape of the solution in the velocity variable. To that end, we adapt the framework of [M. Ohlberger and K. Smetana. A dimensional reduction approach based on the application of reduced basis methods in the framework of hierarchical model reduction. SIAM J. Sci. Comput., 36(2):A714-A736, 2014] and derive initially a parametrized elliptic partial differential equation (PDE) in the velocity variable. Then, we apply ideas of the Reduced Basis method to develop a greedy-algorithm that selects the basis from solutions of the parametrized PDE. Numerical experiments demonstrate the potential of this new method.