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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01236 |
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Table of Contents:
- Parametric data-driven modeling is relevant for many applications in which the model depends on parameters that can potentially vary in both space and time. In this paper, we present a method to obtain a global parametric model based on snapshots of the parameter space. The parameter snapshots are interpolated using the classical univariate Loewner framework and the global bivariate transfer function is extracted using a linear fractional transformation (LFT). Rank bounds for the minimal order of the global realization are also derived. The results are supported by various numerical examples.