4 citations · 4 across the 5 of their papers we have counts for
5 papers
Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems
Tobias Ehring, Bernard Haasdonk
Numerical methods for the optimal feedback control of high-dimensional dynamical systems typically suffer from the curse of dimensionality. In the current presentation, we devise a…
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)…
Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling
Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp +2
In the framework of reduced basis methods, we recently introduced a new certified hierarchical and adaptive surrogate model, which can be used for efficient approximation of input-…
Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds
Patrick Buchfink, Silke Glas, Bernard Haasdonk
Classical model reduction techniques project the governing equations onto linear subspaces of the high-dimensional state-space. For problems with slowly decaying Kolmogorov-n-width…