May 18 – 22, 2026
Virginia Tech
America/New_York timezone

Extensions of data-driven balancing to LQO and QB systems

May 19, 2026, 11:25 AM
25m
Torgersen Hall 1020 (Virginia Tech)

Torgersen Hall 1020

Virginia Tech

Minisymposium Talk Numerical Linear Algebra Tools for Model Order Reduction Numerical Linear Algebra Tools for Model Order Reduction

Speaker

Reetish Padhi (Virginia Tech)

Description

We develop the theoretical framework for extending the quadrature based balanced truncation (QuadBT) method to linear systems with quadratic outputs (LQO). QuadBT which was originally designed for data-driven balanced truncation of standard linear systems with linear outputs only. We show that by sampling the extended impulse responses (kernels) and their derivatives (in the time domain) or the corresponding transfer functions (in the frequency domain), we can construct a reduced order model that mimics the approximation quality of intrusive balanced truncation. At its core, the method can be interpreted as an implicit sampling of the Gramians of the system using input-output observations. We demonstrate a proof of concept for the proposed framework on an example using numerically evaluated data. We also briefly discuss the extension of the QuadBT method for the more involved quadratic-bilinear (QB) system case.

Author

Reetish Padhi (Virginia Tech)

Co-authors

Ion Victor Gosea (Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany) Dr Igor Pontes Duff (CSC group, Max Planck Institute of Dynamics of Complex Technical Systems, Magdeburg, Germany) Serkan Gugercin (Virginia Tech)

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