Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917549473529856 |
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| author | Reiter, Sean Werner, Steffen W. R. |
| author_facet | Reiter, Sean Werner, Steffen W. R. |
| contents | Data-driven reduced-order modeling is an essential component in the computer-aided design of control systems. In this work, we present a novel symmetric Hermite formulation of the quadrature-based balanced truncation algorithm that constructs linear reduced-order models from evaluations of the full-order system's transfer function and its derivative. Significantly, the Hermite formulation preserves desirable qualitative properties of the system used to generate the data, such as state-space Hermiticity and, consequently, asymptotic stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00298 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data Reiter, Sean Werner, Steffen W. R. Numerical Analysis Machine Learning Systems and Control Dynamical Systems Optimization and Control Data-driven reduced-order modeling is an essential component in the computer-aided design of control systems. In this work, we present a novel symmetric Hermite formulation of the quadrature-based balanced truncation algorithm that constructs linear reduced-order models from evaluations of the full-order system's transfer function and its derivative. Significantly, the Hermite formulation preserves desirable qualitative properties of the system used to generate the data, such as state-space Hermiticity and, consequently, asymptotic stability. |
| title | Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data |
| topic | Numerical Analysis Machine Learning Systems and Control Dynamical Systems Optimization and Control |
| url | https://arxiv.org/abs/2606.00298 |