Model Order Reduction. Volume 3, Applications

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 three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science. Preface to the third volume of Model Order Reduction Contents 1 Model reduction in chemical process optimization 2 Model order reduction in mechanical engineering 3 Case studies of model order reduction for acoustics and vibrations 4 Model order reduction in microelectronics 5 Complexity reduction of electromagnetic systems 6 Model reduction in computational aerodynamics 7 Model order reduction in neuroscience 8 Reduced-order modeling for applications to the cardiovascular system 9 From the POD-Galerkin method to sparse manifold models 10 Model order reduction in uncertainty quantification 11 Reduced-order modeling of large-scale network systems 12 Model order reduction and digital twins 13 MOR software Index 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 three-volume handbook covers methods as well as applications.-- Source other than the Library of Congress