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- Max Planck SocietyDE17 papers
- Otto-von-Guericke-Universität MagdeburgDE6 papers
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- Brandenburg University of Technology Cottbus-SenftenbergDE2 papers
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5 papers · 1 filter
Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint
Pawan Goyal, Peter Benner
Measurement noise is an integral part while collecting data of a physical process. Thus, noise removal is a necessary step to draw conclusions from these data, and it often becomes…
Learning reduced order models from data for hyperbolic PDEs
Neeraj Sarna, Peter Benner
Given a set of solution snapshots of a hyperbolic PDE, we are interested in learning a reduced order model (ROM). To this end, we propose a novel decompose then learn approach. We…
Data-driven modeling and control of large-scale dynamical systems in the Loewner framework
Ion Victor Gosea, Charles Poussot-Vassal, Athanasios C. Antoulas
In this contribution, we discuss the modeling and model reduction framework known as the Loewner framework. This is a data-driven approach, applicable to large-scale systems, which…
LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes
Pawan Goyal, Peter Benner
Mathematical modeling is an essential step, for example, to analyze the transient behavior of a dynamical process and to perform engineering studies such as optimization and contro…
Factorization of the Loewner matrix pencil and its consequences
Qiang Zhang, Ion Victor Gosea, Athanasios C. Antoulas
This paper starts by deriving a factorization of the Loewner matrix pencil that appears in the data-driven modeling approach known as the Loewner framework and explores its consequ…