"An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques. "--.

• Preface to the first volume of Model Order Reduction
• Contents
• 1 Model order reduction: basic concepts and notation
• 2 Balancing-related model reduction methods
• 3 Model order reduction based on moment-matching
• 4 Modal methods for reduced order modeling
• 5 Post-processing methods for passivity enforcement
• 6 The Loewner framework for system identification and reduction
• 7 Manifold interpolation
• 8 Vector fitting
• 9 Kernel methods for surrogate modeling
• 10 Kriging: methods and applications
• Index