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Found 6 papers, 1 papers with code
Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
In this work, we address the challenge of efficiently modeling dynamical systems in process engineering.
Data-driven approximation and reduction from noisy data in matrix pencil frameworks
This work aims at tackling the problem of learning surrogate models from noisy time-domain data by means of matrix pencil-based techniques, namely the Hankel and Loewner frameworks.
A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits
In this contribution, we propose a data-driven procedure to fit quadratic-bilinear surrogate models from data.
Data-driven modeling and control of large-scale dynamical systems in the Loewner framework
In this contribution, we discuss the modeling and model reduction framework known as the Loewner framework.
Reduced-order modeling of LPV systems in the Loewner framework
We propose a model reduction method for LPV systems.
Algorithms for the rational approximation of matrix-valued functions
A selection of algorithms for the rational approximation of matrix-valued functions are discussed, including variants of the interpolatory AAA method, the RKFIT method based on approximate least squares fitting, vector fitting, and a method based on low-rank approximation of a block Loewner matrix.
Numerical Analysis Numerical Analysis 41A20, 65D15
