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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
Preface 1. Quick start 2. Parameterized models in physics and engineering 3. Discover the active subspace 4. Exploit the active subspace 5. Active subspaces in action 6. Summary and future directions Bibliography Index.
Preface 1. Quick start 2. Parameterized models in physics and engineering 3. Discover the active subspace 4. Exploit the active subspace 5. Active subspaces in action 6. Summary and future directions Bibliography Index.
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