Two-sided Krylov enhanced proper orthogonal decomposition methods for partial differential equations with variable coefficients

In this paper, new fast computing methods for partial differential equations with variable coefficients are studied and analyzed. They are two kinds of two-sided Krylov enhanced proper orthogonal decomposition (KPOD) methods. First, the spatial discrete scheme of an advection-diffusion equation is obtained by Galerkin approximation. Then, an algorithm based on a two-sided KPOD approach involving the block Arnoldi and block Lanczos processes for the obtained time-varying equations is put forward. Moreover, another type of two-sided KPOD algorithm based on Laguerre orthogonal polynomials in frequency domain is provided. For the two kinds of two-sided KPOD methods, we present a theoretical analysis for the moment matching of the discrete time-invariant transfer function in time domain and give the error bound caused by the reduced-order projection between the Galerkin finite element solution and the approximate solution of the two-sided KPOD method. Finally, the feasibility of four two-sided KPOD algorithms is verified by several numerical results with different inputs and setting parameters.

Automated summary

This paper studies and analyzes new fast computing methods for partial differential equations with variable coefficients, including two kinds of two-sided Krylov enhanced proper orthogonal decomposition (KPOD) methods. The spatial discrete scheme of an advection-diffusion equation is obtained by Galerkin approximation. Then, algorithms based on two-sided KPOD approaches involving block Arnoldi and block Lanczos processes are proposed for the obtained time-varying equations. Moreover, another type of two-sided KPOD algorithm based on Laguerre orthogonal polynomials in frequency domain is provided. The feasibility of four two-sided KPOD algorithms is verified by numerical results with different inputs and setting parameters.