A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications
Historically, optimal transport was about moving a pile of mortar efficiently or transferring the output of an array of steel mines optimally. This type of problem has been found to arise in many different fields of mathematics, science, and engineering-from fluid dynamics to many-electron physics to artificial intelligence-and in the last three ......
This book provides the mathematical foundations needed for designing practical controllers for linear time-invariant systems. The authors accomplish this by incorporating intentional time delays into measurements with the goal of achieving anticipation capabilities, reduction in noise sensitivity, and a fast response. The benefits of these types ......
As experimental data sets have grown and computational power has increased, new tools have been developed that have the power to model new systems and fundamentally alter how current systems are analyzed. This book brings together modern computational tools to provide an accurate understanding of dynamic data. The techniques build on ......
Predicting the future is a difficult task but, as with the weather, it is possible with good models. But how does one predict the far future before the near future is known? Time parallel time integration, also known as PinT (Parallel-in-Time) methods, aims to predict the near and far future simultaneously. In this self-contained book, the first ......
Nonstandard Interaction Domains and Finite Element Discretizations
The book presents the state of the art of nonlocal modeling and discretization and novel analyses of a class of nonstandard nonlocal models. These models have recently become a viable alternative to classical partial differential equations when the latter are unable to capture effects such as discontinuities and multiscale behavior in a system of ......
Uncertainty quantification is an important step in establishing the predictive accuracy of simulation models employed in a broad range of disciplines. The book provides a comprehensive and unified treatment of the mathematical, statistical, and numerical topics required to perform uncertainty analysis for models arising in a wide range of ......
The method of least squares, discovered by Gauss in 1795, is a principal tool for reducing the influence of errors when fitting a mathematical model to given observations. Applications arise in a great number of areas in sciences and engineering. The increased use of automatic data capturing frequently leads to large-scale least squares problems. ......
Set-valued, convex, and nonsmooth analysis are relatively modern branches of mathematical analysis and increasingly relevant in current control theory and control engineering literature. The first book of this scope and at this level, Set-Valued, Convex, and Nonsmooth Analysis in Dynamics and Control serves as a broad introduction to the field and ......
The goal of Algorithmic Mathematics in Machine Learning is to explore several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. In this unique book, the authors: Present machine learning methods, review the underlying mathematics, and provide programming exercises intended to deepen the ......
