Model order reduction : Volume 2: Snapshot-based methods and algorithms

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 second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science. Preface to the second volume of Model Order Reduction Contents 1 Basic ideas and tools for projection-based model reduction of parametric partial differential equations 2 Model order reduction by proper orthogonal decomposition 3 Proper generalized decomposition 4 Reduced basis methods 5 Computational bottlenecks for PROMs: precomputation and hyperreduction 6 Localized model reduction for parameterized problems 7 Data-driven methods for reduced-order modeling Index