716 research outputs found
Galerkin Trial Spaces and Davison-Maki Methods for the Numerical Solution of Differential Riccati Equations
Identification of linear time-invariant systems with Dynamic Mode Decomposition
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 identification properties of DMD. We first show that DMD is invariant under linear transformations in the image of the data matrix. If, in addition, the data are constructed from a linear time-invariant system, then we prove that DMD can recover the original dynamics under mild conditions. If the linear dynamics are discretized with the Runge–Kutta method, then we further classify the error of the DMD approximation and detail that for one-stage Runge–Kutta methods; even the continuous dynamics can be recovered with DMD. A numerical example illustrates the theoretical findings
Classical System Theory Revisited for Turnpike in Standard State Space Systems and Impulse Controllable Descriptor Systems
Convolutional Neural Networks for Very Low-dimensional LPV Approximations of Incompressible Navier-Stokes Equations
Robust Output-Feedback Stabilization for Incompressible Flows using Low-Dimensional H<sub>∞</sub>-Controllers
Best Practices for Replicability, Reproducibility and Reusability of Computer-based Experiments exemplified by Model Reduction Software
Non-intrusive Time Galerkin POD for Optimal Control of a Fixed-Bed Reactor for CO<sub>2</sub> Methanation
Moment-Matching Based Model Reduction for Navier–Stokes Type Quadratic-Bilinear Descriptor Systems
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