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

Sean Reiter; Steffen W. R. Werner

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arXiv · 2606.00298

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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Sean Reiter, Steffen W. R. Werner. 2026-05-29. Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data. https://arxiv.org/abs/2606.00298

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