Herkert, RobinBuchfink, PatrickHaasdonk, Bernard2025-05-2820241572-90441019-7168http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-164810https://elib.uni-stuttgart.de/handle/11682/16481https://doi.org/10.18419/opus-16462Classical model order reduction (MOR) for parametric problems may become computationally inefficient due to large sizes of the required projection bases, especially for problems with slowly decaying Kolmogorov n -widths. Additionally, Hamiltonian structure of dynamical systems may be available and should be preserved during the reduction. In the current presentation, we address these two aspects by proposing a corresponding dictionary-based, online-adaptive MOR approach. The method requires dictionaries for the state-variable, non-linearities, and discrete empirical interpolation (DEIM) points. During the online simulation, local basis extensions/simplifications are performed in an online-efficient way, i.e., the runtime complexity of basis modifications and online simulation of the reduced models do not depend on the full state dimension. Experiments on a linear wave equation and a non-linear Sine-Gordon example demonstrate the efficiency of the approach.enCC BYinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/510article2025-01-24