Overview
- Offers 4 crucial modern topics and their synergistic interaction: model order reduction, efficient solvers, high dimensional approximation and applications
- Includes contributions, upon invitation, from world experts
- Provides high quality, inspiring and innovative chapters spanning a broad class of critical topics
Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 137)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
About the editors
Dr. Marta D'Elia is a staff member at Sandia National Laboratories. She graduated with honors in Mathematical Engineering at Politecnico di Milano, 2007, and she has a Phd in Applied Mathematics from Emory University, 2011. She was a postdoctoral fellow at Florida State University from 2012 to 2014. Her research deals with computational science and engineering, and it is mainly focused on modeling and simulation of nonlocal problems. She authored more than 30 research papers, she is the principal investigator of a Laboratory Directed R&D grant, associate editor of the SIAM Journal on Scientific Computing and organizer of several international conferences.
Max Gunzburger, a Distinguished Professor at Florida State University, has advised 46 PhD students and 36 postdocs, published over 300 journal articles, conducted research funded by several US agencies, consulted for government and private labs, and served as EIC of two SIAM journals. His research interests include numerical analysis, finite elements, control, grid generation, and differential and integral equations with applications in mechanics, diffusion, climate, superconductivity, subsurface flows, etc.
Gianluigi Rozza is a Professor of Numerical Analysis and Scientific Computing at SISSA, the International School for Advanced Studies, in Trieste, Italy, where he is a lecturer and coordinator of the SISSA doctoral program in Mathematical Analysis, Modelling and Applications, and director delegate for innovation, valorisation of research and technology transfer. His research chiefly focuses on developing reduced order methods. The author of more than 120 scientific publications, Principal Investigator of the European Research Council AROMA-CFD project. Winner of the 2004 Bill Morton CFD Prize (Oxford University); ECCOMAS Phd Award 2006; Springer CSE prize in 2009; and ECCOMAS Jacques Louis Lions Award in 2014.
Bibliographic Information
Book Title: Quantification of Uncertainty: Improving Efficiency and Technology
Book Subtitle: QUIET selected contributions
Editors: Marta D'Elia, Max Gunzburger, Gianluigi Rozza
Series Title: Lecture Notes in Computational Science and Engineering
DOI: https://doi.org/10.1007/978-3-030-48721-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-48720-1Published: 31 July 2020
Softcover ISBN: 978-3-030-48723-2Published: 01 August 2021
eBook ISBN: 978-3-030-48721-8Published: 30 July 2020
Series ISSN: 1439-7358
Series E-ISSN: 2197-7100
Edition Number: 1
Number of Pages: XI, 282
Number of Illustrations: 23 b/w illustrations, 90 illustrations in colour
Topics: Computational Mathematics and Numerical Analysis, Mathematical and Computational Engineering, Simulation and Modeling