Quantification of Uncertainty: QUIET selected contributions (1st ed. 2020) (Lecture Notes in Computational Science and Engineering #137)

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Synopsis
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.
Copyright:
2020

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9783030487218
Related ISBNs:
9783030487201
Publisher:
Springer International Publishing
Date of Addition:
Copyrighted By:
Springer
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Technology, Mathematics and Statistics
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.
Edited by:
Marta D'Elia
Edited by:
Max Gunzburger
Edited by:
Gianluigi Rozza

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