Quantification of Uncertainty: Improving Efficiency and Technology - QUIET selected contributions (Hardcover, 1st ed. 2020)

This book explores four guiding themes - reduced order modelling, high dimensional problems, efficient algorithms, and applications - by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book's content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Lecture Notes in Computational Science and Engineering, 137
Release date:	August 2020
First published:	2020
Editors:	Marta D'Elia • Max Gunzburger • Gianluigi Rozza
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	282
Edition:	1st ed. 2020
ISBN-13:	978-3-03-048720-1
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
LSN:	3-03-048720-2
Barcode:	9783030487201

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