Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies - Softcover

Constantine, Paul G.

Softcover

ISBN 10: 1611973856 ISBN 13: 9781611973853

Publisher: Society for Industrial and Applied Mathematics, 2015

Synopsis

Scientists and engineers use computer simulations to study relationships between a model's input parameters and its outputs. However, thorough parameter studies are challenging, if not impossible, when the simulation is expensive and the model has several inputs. To enable studies in these instances, the engineer may attempt to reduce the dimension of the model's input parameter space.

Active subspaces are an emerging set of dimension reduction tools that identify important directions in the parameter space. This book describes techniques for discovering a model's active subspace and proposes methods for exploiting the reduced dimension to enable otherwise infeasible parameter studies. Readers will find new ideas for dimension reduction, easy-to-implement algorithms, and several examples of active subspaces in action.

Audience: This book is intended for researchers and graduate students in computational science, applied mathematics, statistics, and engineering.

Contents: Preface; Chapter 1: Quick Start; Chapter 2: Parameterized Models in Physics and Engineering; Chapter 3: Discover the Active Subspace; Chapter 4: Exploit the Active Subspace; Chapter 5: Active Subspaces in Action; Chapter 6: Summary and Future Directions; Bibliography; Index.

About this title

Book Description:

Studying the relationship between a model's input parameters and its outputs is challenging when the simulation is expensive and the model has several inputs. This book presents techniques and algorithms for discovering and exploiting active subspaces: an emerging set of tools for dimension reduction of the model's input parameter space.

About the Author:

Paul G. Constantine is the Ben L. Fryrear Assistant Professor of Applied Mathematics and Statistics at Colorado School of Mines. He received his Ph.D. from Stanford's Institute for Computational and Mathematical Engineering and spent two years as the von Neumann Fellow at the Sandia National Laboratories' Computer Science Research Institute. His research interests include uncertainty quantification and dimension reduction for large-scale computer simulations.

About this edition

• Publisher: Society for Industrial and Applied Mathematics
• Publication date: 2015
• ISBN 10: 1611973856
• ISBN 13: 9781611973853
• Binding: Paperback
• Number of pages: 110