Handbook of Uncertainty Quantification

Artikelnummer: 978-3-319-12384-4
Einband: Set mit div. Artikeln (Set)
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The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.

The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.

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VerlagSpringer EN
EinbandSet mit div. Artikeln (Set)
Erscheinungsjahr2017
Seitenangabe2078 S.
AusgabekennzeichenEnglisch
AbbildungenXXV, 2053 p. 520 illus., 424 illus. in color. In 3 volumes, not available separately., farbige Illustrationen, schwarz-weiss Illustrationen
MasseH23.5 cm x B15.5 cm 3'750 g
CoverlagSpringer (Imprint/Brand)
Auflage1st ed. 2017
AutorGhanem, Roger (Hrsg.) / Higdon, David (Hrsg.) / Owhadi, Houman (Hrsg.)

Über den Autor Roger (Hrsg.) Ghanem

Roger Ghanem is the Gordon S. Marshall Professor of Engineering at the University of Southern California where he holds joint appointments in the Departments of Civil & Environmental Engineering and Mechanical & Aerospace Engineering. David Higdon is Scientists and Group Leader in Statistical Sciences at Los Alamos National Laboratories. He has developed statistical concepts and methodologies that are uniquely adapted to modeling and simulation and computationally intensive numerical modelsHouman Owhadi is a Professor of Applied & Computational Mathematics and Control & Dynamical Systems at the California Institute of Technology.

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