ENGLISH

Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions

By Max Gunzburger , Gianluigi Rozza , Marta D'Elia

Book information

Publisher
Springer International Publishing;Springer
Year
2020
ISBN
9783030487201, 9783030487218
DOI
10.1007/978-3-030-48721-8
Language
english
Format
PDF
Filesize
10 MB (10914543 bytes)
Series
Lecture Notes in Computational Science and Engineering 137
Edition
1st ed.
Pages
XI, 282\290
Time added
2021-01-06 05:41:07
Mathematics; Computational Mathematics and Numeric

Description

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.

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