Balanced truncation model order reduction in limited time intervals for large systems

• Published in: Advances in computational mathematics Vol. 44; no. 6; pp. 1821 - 1844
• Main Author: Kürschner, Patrick
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2018; Springer Nature B.V
• Subjects: Approximation; Computational mathematics; Computational Mathematics and Numerical Analysis; Computational Science and Engineering; Eigenvalues; Exponential functions; Intervals; Mathematical and Computational Biology; Mathematical Modeling and Industrial Mathematics; Mathematical models; Mathematics; Mathematics and Statistics; Model accuracy; Model reduction; Numerical methods; Reduced order models; Subspace methods; Visualization; Rational Krylov subspaces; 93A15; Balanced truncation; 65F60; 15A24; 15A16; 93C; Model order reduction; Lyapunov equation; Matrix exponential; 15A18
• ISSN: 1019-7168; 1572-9044
• DOI: 10.1007/s10444-018-9608-6

In this article we investigate model order reduction of large-scale systems using time-limited balanced truncation, which restricts the well known balanced truncation framework to prescribed finite time intervals. The main emphasis is on the efficient numerical realization of this model reduction approach in case of large system dimensions. We discuss numerical methods to deal with the resulting matrix exponential functions and Lyapunov equations which are solved for low-rank approximations. Our main tool for this purpose are rational Krylov subspace methods. We also discuss the eigenvalue decay and numerical rank of the solutions of the Lyapunov equations. These results, and also numerical experiments, will show that depending on the final time horizon, the numerical rank of the Lyapunov solutions in time-limited balanced truncation can be smaller compared to standard balanced truncation. In numerical experiments we test the approaches for computing low-rank factors of the involved Lyapunov solutions and illustrate that time-limited balanced truncation can generate reduced order models having a higher accuracy in the considered time region.