Publisher's Synopsis
- system-theoretic methods, such as balanced truncation, Hankel norm approximation, and reduced-basis methods;
- data-driven methods, including Loewner matrix and pencil-based approaches, dynamic mode decomposition, and kernel-based methods;
- surrogate modeling for design and optimization, with special emphasis on control and data assimilation;
- model reduction methods in applications, such as control and network systems, computational electromagnetics, structural mechanics, and fluid dynamics; and
- model order reduction software packages and benchmarks.