Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications

by Baasansuren Jadamba, Akhtar Khan, Fabio Raciti, and Joachim Gwinner

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Book cover for Uncertainty Quantification in Variational Inequalities

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Uncertainty Quantification (UQ) is an emerging and extremely active research discipline which aims to quantitatively treat any uncertainty in applied models. The primary objective of Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications is to present a comprehensive treatment of UQ in variational inequalities and some of its generalizations emerging from various network, economic, and engineering models. Some of the developed techniques also apply to machine learning, neural networks, and related fields.

Features

  • First book on UQ in variational inequalities emerging from various network, economic, and engineering models
  • Completely self-contained and lucid in style
  • Aimed for a diverse audience including applied mathematicians, engineers, economists, and professionals from academia
  • Includes the most recent developments on the subject which so far have only been available in the research literature
  • ISBN13 9781351857659
  • Publish Date 24 December 2021 (first published 21 December 2021)
  • Publish Status Active
  • Publish Country GB
  • Publisher Taylor & Francis Ltd
  • Imprint Chapman and Hall
  • Format eBook
  • Pages 386
  • Language English