51 citations · 204 across the 20 of their papers we have counts for
6 papers · 1 filter
Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators
Jonas Nicodemus, Jonas Kneifl, Jörg Fehr +1
We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising…
Identification of linear time-invariant systems with Dynamic Mode Decomposition
Jan Heiland, Benjamin Unger
Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identificati…
Decomposition of flow data via gradient-based transport optimization
Felix Black, Philipp Schulze, Benjamin Unger
We study an optimization problem related to the approximation of given data by a linear combination of transformed modes. In the simplest case, the optimization problem reduces to…
Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction
Felix Black, Philipp Schulze, Benjamin Unger
We propose a new hyper-reduction method for a recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited fo…
Parametric model reduction via rational interpolation along parameters
Ion Victor Gosea, Serkan Gugercin, Benjamin Unger
We present a novel projection-based model reduction framework for parametric linear time-invariant systems that allows interpolating the transfer function at a given frequency poin…
Passivity preserving model reduction via spectral factorization
Tobias Breiten, Benjamin Unger
We present a novel model-order reduction (MOR) method for linear time-invariant systems that preserves passivity and is thus suited for structure-preserving MOR for port-Hamiltonia…