
Model Reduction of Complex Dynamical Systems
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- 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.
This volume will be an ideal resource for graduate students and researchers in all areas of model reduction, as well as those working in applied mathematics and theoretical informatics.
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