Quantification of Uncertainty: Improving Efficiency and Technology

QUIET selected contributions

  • Marta D'Elia
  • Max Gunzburger
  • Gianluigi Rozza

Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 137)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Ehsan Adeli, Bojana Rosić, Hermann G. Matthies, Sven Reinstädler
    Pages 1-13
  3. Babak Maboudi Afkham, Nicolò Ripamonti, Qian Wang, Jan S. Hesthaven
    Pages 67-99
  4. Fabrizio Garotta, Nicola Demo, Marco Tezzele, Massimo Carraturo, Alessandro Reali, Gianluigi Rozza
    Pages 153-170
  5. Alessandro Boccadifuoco, Alessandro Mariotti, Katia Capellini, Simona Celi, Maria Vittoria Salvetti
    Pages 171-192
  6. Alessandro Anderlini, Maria Vittoria Salvetti, Antonio Agresta, Luca Matteucci
    Pages 193-215
  7. Matthieu Bulté, Jonas Latz, Elisabeth Ullmann
    Pages 241-272
  8. Back Matter
    Pages 273-282

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.


Uncertainty Quantification Partial Differential Equations Reduced Order modelling Computational Mechanics High dimensional problems

Editors and affiliations

  • Marta D'Elia
    • 1
  • Max Gunzburger
    • 2
  • Gianluigi Rozza
    • 3
  1. 1.Computational MultiscaleSandia National LaboratoriesAlbuquerqueUSA
  2. 2.Dept of Scientific ComputingFlorida State UnivTallahasseeUSA
  3. 3.SISSA mathLab, Mathematics AreaInternational School for Advanced StudieTriesteItaly

Bibliographic information

  • DOI
  • Copyright Information The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
  • Print ISBN 978-3-030-48720-1
  • Online ISBN 978-3-030-48721-8
  • Series Print ISSN 1439-7358
  • Series Online ISSN 2197-7100
  • Buy this book on publisher's site