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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.

Produktbeschreibung
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.
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Autorenporträt
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.