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

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