Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
arXiv:2606.00298v1 Announce Type: cross 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.
The paper presents a novel method for improving data-driven reduced-order modeling, which is crucial for efficient control system design as computational demands grow.
Sophisticated readers should care because more efficient and accurate model reduction techniques are vital for developing complex AI systems and control systems, reducing computational overhead and improving reliability.
The ability to preserve critical qualitative properties like asymptotic stability through a new Hermite formulation significantly enhances the robustness and applicability of reduced-order models.
- · AI researchers
- · control system engineers
- · developers of complex autonomous systems
- · industries using digital twins
- · developers of less efficient model reduction techniques
Improved efficiency and accuracy in modeling complex linear dynamical systems, especially in areas using derivative data.
Faster development cycles and deployment of more reliable AI-driven control systems across various industries, from manufacturing to aerospace.
Acceleration of research into more complex, data-intensive AI models that were previously computationally prohibitive, leading to new applications.
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