In this article, we present a deep learning-based reduced order model (DL-ROM) for
predicting the fluid forces and unsteady vortex shedding patterns. We consider the flow past …

We investigate the sensitivity of reduced order models (ROMs) to training data spatial
resolution as well as sampling rate. In particular, we consider proper orthogonal …

We investigate the combination of Isogeometric Analysis (IGA) and proper orthogonal
decomposition (POD) based on the Galerkin method for model order reduction of linear …

Along the ideas of Curtain and Glover (in: Bart, Gohberg, Kaashoek (eds) Operator theory
and systems, Birkhäuser, Boston, 1986), we extend the balanced truncation method for …

We prove the H-infinity error bounds for Lyapunov balanced truncation and for optimal
Hankel norm approximation under the assumption that the Hankel operator is nuclear. This …

In this paper, we put forth a long short-term memory (LSTM) nudging framework for the
enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements …

We give an algorithmic approach to the approximative solution of operator Lyapunov
equations for controllability. Motivated by the successfully applied alternating direction …

The solution of large-scale matrix algebraic Riccati equations is important for instance in
control design and model reduction and remains an active area of research. We consider a …

In this paper, a new model reduction technique mixing the balanced and modal truncations
is proposed for power systems. Usually, only the power transmission lines are approximated …

Model reduction by balanced truncation for bounded real and positive real input-stateoutput
systems, known as bounded real balanced truncation and positive real balanced truncation …