Dynamical systems are a principal tool in the modeling, prediction, and control of a wide range of complex phenomena. As the need for improved accuracy leads to larger and more complex dynamical systems, direct simulation often becomes the only available strategy for accurate prediction or control, inevitably creating a considerable burden on computational resources. This is the main context where one considers model reduction, seeking to replace large systems of coupled differential and algebraic equations that constitute high fidelity system models with substantially fewer equations that are crafted to control the loss of fidelity that order reduction may induce in the system response.
Interpolatory methods are among the most widely used model reduction techniques, and this textbook is the first comprehensive analysis of this approach available in a form readily accessible to practitioners.
Interpolatory Methods for Model Reduction
• provides a single, extensive resource for interpolatory model reduction techniques;
• contains state-of-the-art methods, which have matured significantly over the last two decades; and
• covers both classical projection frameworks for model reduction and data-driven, nonintrusive frameworks.