81 citations
- Max Planck SocietyDE17 papers
- Otto-von-Guericke-Universität MagdeburgDE3 papers
- Rice UniversityUS3 papers
- Baylor College of MedicineUS1 paper
- Brandenburg University of Technology Cottbus-SenftenbergDE1 paper
- Courant Institute of Mathematical SciencesUS1 paper
- Czech Technical University in PragueCZ1 paper
- Duke UniversityUS1 paper
- New York UniversityUS1 paper
- Office National d'Études et de Recherches AérospatialesFR1 paper
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8 papers · 1 filter
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…
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…
Efficient and Accurate Algorithms for Solving the Bethe-Salpeter Eigenvalue Problem for Crystalline Systems
Peter Benner, Carolin Penke
Optical properties of materials related to light absorption and scattering are explained by the excitation of electrons. The Bethe-Salpeter equation is the state-of-the-art approac…
Hyperbolic Discretization via Riemann Invariants
Sara Grundel, Michael Herty
We are interested in numerical schemes for the simulation of large scale gas networks. Typical models are based on the isentropic Euler equations with realistic gas constant. The n…
Rational approximation of the absolute value function from measurements: a numerical study of recent methods
Ion Victor Gosea, Athanasios C. Antoulas
In this work, we propose an extensive numerical study on approximating the absolute value function. The methods presented in this paper compute approximants in the form of rational…
A Non-stationary Thermal-Block Benchmark Model for Parametric Model Order Reduction
Stephan Rave, Jens Saak
In this contribution we aim to satisfy the demand for a publicly available benchmark for parametric model order reduction that is scalable both in degrees of freedom as well as par…