Dynamical reduced basis methods for Hamiltonian systems

TL;DR

This paper introduces a nonlinear, structure-preserving model reduction technique for Hamiltonian systems that maintains geometric properties and enables efficient, accurate simulations of wave and transport phenomena.

Contribution

It develops a dynamical reduced basis method that evolves in time, preserving symplectic structure and providing error estimates for Hamiltonian systems.

Findings

The method achieves linear complexity in the full model dimension.

Error estimates relate to projection errors of the full solution.

Numerical schemes converge with the order of the Runge-Kutta integrator.

Abstract

We consider model order reduction of parameterized Hamiltonian systems describing nondissipative phenomena, like wave-type and transport dominated problems. The development of reduced basis methods for such models is challenged by two main factors: the rich geometric structure encoding the physical and stability properties of the dynamics and its local low-rank nature. To address these aspects, we propose a nonlinear structure-preserving model reduction where the reduced phase space evolves in time. In the spirit of dynamical low-rank approximation, the reduced dynamics is obtained by a symplectic projection of the Hamiltonian vector field onto the tangent space of the approximation manifold at each reduced state. A priori error estimates are established in terms of the projection error of the full model solution onto the reduced manifold. For the temporal discretization of the reduced dynamics we employ splitting techniques. The reduced basis satisfies an evolution equation on the manifold of symplectic and orthogonal rectangular matrices having one dimension equal to the size of the full model. We recast the problem on the tangent space of the matrix manifold and develop intrinsic temporal integrators based on Lie group techniques together with explicit Runge-Kutta (RK) schemes. The resulting methods are shown to converge with the order of the RK integrator and their computational complexity depends only linearly on the dimension of the full model, provided the evaluation of the reduced flow velocity has a comparable cost.