Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems
An essential tool in data-driven modeling of dynamical systems from frequency response measurements is the barycentric form of the underlying rational transfer function. In this work, we propose structured barycentric forms for modeling dynamical systems with second-order time derivatives using thei...
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| Veröffentlicht in: | Advances in computational mathematics 2024-04, Vol.50 (2), Article 26 |
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| creator | Gosea, Ion Victor Gugercin, Serkan Werner, Steffen W. R. |
| description | An essential tool in data-driven modeling of dynamical systems from frequency response measurements is the barycentric form of the underlying rational transfer function. In this work, we propose structured barycentric forms for modeling dynamical systems with second-order time derivatives using their frequency domain input-output data. By imposing a set of interpolation conditions, the systems’ transfer functions are rewritten in different barycentric forms using different parametrizations. Loewner-like algorithms are developed for the explicit computation of second-order systems from data based on the developed barycentric forms. Numerical experiments show the performance of these new structured data-driven modeling methods compared to other interpolation-based data-driven modeling techniques from the literature. |
| doi_str_mv | 10.1007/s10444-024-10118-7 |
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| subjects | Algorithms Computational Mathematics and Numerical Analysis Computational Science and Engineering Dynamical systems Frequency response Interpolation Mathematical analysis Mathematical and Computational Biology Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics MORe 2022 Structured data Transfer functions Visualization |
| title | Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems |
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