Snapshot-Based Methods and Algorithms / Edition 1

by Peter Benner, et al.

Overview

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.

Product Details

ISBN-13:	9783110671407
Publisher:	De Gruyter
Publication date:	12/16/2020
Pages:	356
Product dimensions:	6.69(w) x 9.45(h) x (d)
Age Range:	18 Years

About the Author

Benner, MPI, GE; Grivet-T., POLITO, Quarteroni, Politec., Rozza, SISSA, IT; Schilders, TU Eind., NE; Silveira, INESC-ID, PT

Table of Contents

Preface to the second volume of Model Order Reduction V

1 Basic ideas and tools for projection-based model reduction of parametric partial differential equations Gianluigi Rozza Martin Hess Giovanni Stabile Marco Tezzele Francesco Ballarin 1

2 Model order reduction by proper orthogonal decomposition Carmer Gräßle Michael Hinze Stefan Volkwein 47

3 Proper generalized decomposition Francisco Chinesta Pierre Ladevélze 97

4 Reduced basis methods Yvon Maday Anthony T. Patera 139

5 Computational bottlenecks for PROMs: precomputation and hyperreduction Charbel Farhat Sebastian Grimberg Andrea Manzoni Alfio Quarteroni 181

6 Localized model reduction for parameterized problesm Andreas Buhr Laura lapichino Mario Ohlberger Stephan Rave Felix Schindler Kathrin Smetana 245

7 Data-driven methods for reduced-order modeling Steven L. Brunton J. Nathan Kutz 307

Index 345