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Introduces graduate students to the state of the art in this fast-developing field of applied mathematics.
Proceedings of the NATO ARW, Shoresh, Israel, from 30 June to 4 July 2003
The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning" for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.
The objective of this book is to navigate beginning graduate students in mathematics and engineering through a mature field of multiscale problems in homogenization theory and to provide an idea of its broad scope. An overview of a wide spectrum of homogenization techniques ranging from classical two-scale asymptotic expansions to Gamma convergence and the rapidly developing field of stochastic homogenization is presented. The mathematical proofs and definitions are supplemented with intuitive explanations and figures to make them easier to follow. A blend of mathematics and examples from materials science and engineering is designed to teach a mixed audience of mathematical and non-mathematical students.
The IUTAM Symposium on Mechanical and Electromagnetic Waves in Structured Media took place at the University of Sydney from January 18- 22, 1999. It brought together leading researchers from eleven countries for a week-long meeting, with the aim of providing cross-links between the com- nities studying related problems involving elastic and electromagnetic waves in structured materials. After the meeting, participants were invited to submit articles based on their presentations, which were refereed and assembled to constitute these Proceedings. The topics covered here represent areas at the forefront of research intoelastic and electromagnetic waves. They include effect of nonlinearity, diff...
This monograph is devoted to the study of multiscale model reduction methods from the point of view of multiscale finite element methods. Multiscale numerical methods have become popular tools for modeling processes with multiple scales. These methods allow reducing the degrees of freedom based on local offline computations. Moreover, these methods allow deriving rigorous macroscopic equations for multiscale problems without scale separation and high contrast. Multiscale methods are also used to design efficient solvers. This book offers a combination of analytical and numerical methods designed for solving multiscale problems. The book mostly focuses on methods that are based on multiscale finite element methods. Both applications and theoretical developments in this field are presented. The book is suitable for graduate students and researchers, who are interested in this topic.
The contributions in this volume aim to deepen understanding of some of the current research problems and theories in modern topics such as calculus of variations, optimization theory, complex analysis, real analysis, differential equations, and geometry. Applications to these areas of mathematics are presented within the broad spectrum of research in Engineering Science with particular emphasis on equilibrium problems, complexity in numerical optimization, dynamical systems, non-smooth optimization, complex network analysis, statistical models and data mining, and energy systems. Additional emphasis is given to interdisciplinary research, although subjects are treated in a unified and self-...
Multiscale problems naturally pose severe challenges for computational science and engineering. The smaller scales must be well resolved over the range of the larger scales. Challenging multiscale problems are very common and are found in e.g. materials science, fluid mechanics, electrical and mechanical engineering. Homogenization, subgrid modelling, heterogeneous multiscale methods, multigrid, multipole, and adaptive algorithms are examples of methods to tackle these problems. This volume is an overview of current mathematical and computational methods for problems with multiple scales with applications in chemistry, physics and engineering.
Even a hundred years after its discovery, superconductivity continues to bring us new surprises, from superconducting magnets used in MRI to quantum detectors in electronics. 100 Years of Superconductivity presents a comprehensive collection of topics on nearly all the subdisciplines of superconductivity. Tracing the historical developments in supe
This is an autobiography and an exposition on the contributions and personalities of many of the leading researchers in mathematics and physics with whom Dr Krishna Alladi, Professor of Mathematics at the University of Florida, has had personal interaction with for over six decades. Discussions of various aspects of the physics and mathematics academic professions are included.Part I begins with the author's unusual and frequent introductions as a young boy to scientific luminaries like Nobel Laureates Niels Bohr, Murray Gell-Mann, and Richard Feynman, in the company of his father, the scientist Alladi Ramakrishnan. Also in Part I is an exciting account of how the author started his research...