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PreprintarXiv licencearXiv

Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data

  • Sean Reiter
  • Steffen W. R. Werner
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@misc{reiter2026symmetric,
  title = {{Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data}},
  author = {Sean Reiter and Steffen W. R. Werner},
  howpublished = {arXiv},
  year = {2026},
  month = jun,
  doi = {10.48550/arxiv.2606.00298},
  eprint = {2606.00298},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.00298},
}
RIS
TY  - UNPB
TI  - Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
AU  - Sean Reiter
AU  - Steffen W. R. Werner
PY  - 2026
DA  - 2026/06/01
PB  - arXiv
DO  - 10.48550/arxiv.2606.00298
UR  - https://arxiv.org/abs/2606.00298
AB  - 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.
ER  -