9 citations · 24 across the 15 of their papers we have counts for
6 papers · 1 filter
Model reduction on manifolds: A differential geometric framework
Patrick Buchfink, Silke Glas, Bernard Haasdonk +1
Using nonlinear projections and preserving structure in model order reduction (MOR) are currently active research fields. In this paper, we provide a novel differential geometric f…
Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds
Patrick Buchfink, Silke Glas, Bernard Haasdonk
For projection-based linear-subspace model order reduction (MOR), it is well known that the Kolmogorov n-width describes the best-possible error for a reduced order model (ROM) of…
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds
Harsh Sharma, Hongliang Mu, Patrick Buchfink +3
This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical symplectic model…
Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems
Robin Herkert, Patrick Buchfink, Bernard Haasdonk
Classical model order reduction (MOR) for parametric problems may become computationally inefficient due to large sizes of the required projection bases, especially for problems wi…
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)…