5 papers
Randomized Symplectic Model Reduction: One-Pass Basis Construction and Sketched Hamiltonian Assembly
Robin Herkert, Bernard Haasdonk, Jörg Fehr
Symplectic model order reduction of Hamiltonian systems yields reduced dynamics that conserve the reduced Hamiltonian along exact trajectories, avoiding the artificial energy drift…
Petrov-Galerkin operator inference with application to stability-encouraging identification
Johannes Rettberg, Jonas Nicodemus, Harsh Sharma +3
Data-driven model order reduction methods such as operator inference enable the efficient construction of reduced-order models directly from high-dimensional time-domain data. Stan…
Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems
Robin Herkert, Patrick Buchfink, Bernard Haasdonk +2
Solving high-dimensional dynamical systems in multi-query or real-time applications requires efficient surrogate modelling techniques, as e.g., achieved via model order reduction (…
Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems
Johannes Rettberg, Dominik Wittwar, Patrick Buchfink +3
Projection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error c…
Randomized Symplectic Model Order Reduction for Hamiltonian Systems
Robin Herkert, Patrick Buchfink, Bernard Haasdonk +2
Simulations of large scale dynamical systems in multi-query or real-time contexts require efficient surrogate modelling techniques, as e.g. achieved via Model Order Reduction (MOR)…