Fully adaptive structure-preserving hyper-reduction of parametric Hamiltonian systems

TL;DR

This paper introduces a fully adaptive, structure-preserving hyper-reduction method for parametric Hamiltonian systems, enabling efficient, accurate, and geometry-preserving reduced models that adapt in real-time to complex dynamics.

Contribution

It develops a novel adaptive approach combining symplectic low-rank approximation with hyper-reduction and parameter sampling, preserving geometric structure without prior dynamic information.

Findings

Enhanced accuracy of reduced models demonstrated in numerical experiments.

Adaptive models outperform non-adaptive counterparts in efficiency and fidelity.

Method maintains Hamiltonian structure and linear computational complexity.

Abstract

Model order reduction provides low-complexity high-fidelity surrogate models that allow rapid and accurate solutions of parametric differential equations. The development of reduced order models for parametric \emph{nonlinear} Hamiltonian systems is challenged by several factors: (i) the geometric structure encoding the physical properties of the dynamics; (ii) the slowly decaying Kolmogorov $n$-width of conservative dynamics; (iii) the gradient structure of the nonlinear flow velocity; (iv) high variations in the numerical rank of the state as a function of time and parameters. We propose to address these aspects via a structure-preserving adaptive approach that combines symplectic dynamical low-rank approximation with adaptive gradient-preserving hyper-reduction and parameters sampling. Additionally, we propose to vary in time the dimensions of both the reduced basis space and the hyper-reduction space by monitoring the quality of the reduced solution via an error indicator related to the projection error of the Hamiltonian vector field. The resulting adaptive hyper-reduced models preserve the geometric structure of the Hamiltonian flow, do not rely on prior information on the dynamics, and can be solved at a cost that is linear in the dimension of the full order model and linear in the number of test parameters. Numerical experiments demonstrate the improved performances of the fully adaptive models compared to the original and reduced models.