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.


  • 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

part I|172 pages

Variational Inequalities

chapter 2Chapter 1|24 pages


chapter Chapter 2|12 pages


chapter Chapter 3|18 pages

Projections on Convex Sets

chapter Chapter 4|50 pages

Variational and Quasi-Variational Inequalities

part II|104 pages

Uncertainty Quantification

chapter |3 pages

Prologue on Uncertainty Quantification

chapter Chapter 7|20 pages

Expected Residual Minimization (ERM)

chapter Chapter 8|44 pages

Stochastic Approximation Approach

part III|68 pages


chapter 278Chapter 9|14 pages

Uncertainty Quantification in Electric Power Markets

chapter Chapter 10|14 pages

Uncertainty Quantification in Migration Models

chapter Chapter 11|14 pages

Uncertainty Quantification in Nash Equilibrium Problems