Symplectic Model Reduction of Hamiltonian Systems
For computational scientists simulating Hamiltonian systems, this method provides a structure-preserving reduction that outperforms existing approaches for long-time integration.
This paper proposes proper symplectic decomposition (PSD) with symplectic Galerkin projection for model reduction of large-scale Hamiltonian systems, preserving symplectic structure, energy, and stability. Numerical tests on linear and nonlinear wave equations demonstrate improved long-time integration stability and accuracy over classical POD-Galerkin.
In this paper, a symplectic model reduction technique, proper symplectic decomposition (PSD) with symplectic Galerkin projection, is proposed to save the computational cost for the simplification of large-scale Hamiltonian systems while preserving the symplectic structure. As an analogy to the classical proper orthogonal decomposition (POD)-Galerkin approach, PSD is designed to build a symplectic subspace to fit empirical data, while the symplectic Galerkin projection constructs a reduced Hamiltonian system on the symplectic subspace. For practical use, we introduce three algorithms for PSD, which are based upon: the cotangent lift, complex singular value decomposition, and nonlinear programming. The proposed technique has been proven to preserve system energy and stability. Moreover, PSD can be combined with the discrete empirical interpolation method to reduce the computational cost for nonlinear Hamiltonian systems. Owing to these properties, the proposed technique is better suited than the classical POD-Galerkin approach for model reduction of Hamiltonian systems, especially when long-time integration is required. The stability, accuracy, and efficiency of the proposed technique are illustrated through numerical simulations of linear and nonlinear wave equations.