Model Order Reduction: Methods. Volume 1: System- and Data-Driven Methods and Algorithms

Description

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