Active Subspaces by Paul G. Constantine - ISBN: 9781611973853
Paperback
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.

Active Subspaces

Emerging Ideas for Dimension Reduction in Parameter Studies

$112.43

  • Paperback

    109 pages

  • Release Date

    30 March 2015

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Summary

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.
  • Several examples of active subspaces in action.

    Book Details

    ISBN-13:9781611973853
    ISBN-10:1611973856
    Author:Paul G. Constantine
    Publisher:Society for Industrial & Applied Mathematics,U.S.
    Imprint:Society for Industrial & Applied Mathematics,U.S.
    Format:Paperback
    Number of Pages:109
    Release Date:30 March 2015
    Weight:225g
    Dimensions:152mm x 229mm
    Series:SIAM Spotlights
    A-Format
    B-Format
    Active Subspaces by Paul G. Constantine - ISBN: 9781611973853
    152 × 229 mm
    C-Format
    A4
    mm / in
    About The Author

    Paul G. Constantine

    Paul G. Constantine is the Ben L. Fryrear Assistant Professor of Applied Mathematics and Statistics at Colorado School of Mines. He received his PhD 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.

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