The Principles of Circular Accelerators Storage Rings

類似書籍推薦給您

This book is a basic introduction to the principles of circular particle accelerators and storage rings. Particle accelerators used to be the exclusive province of physicists exploring the structure of the most fundamental constituents of matter. The text presented here starts with a historical introduction to the field and an outline of the basic concepts of particle acceleration and phase focusing. It goes on to give more details of how the transverse and longitudinal motions of the particle beam can be analyzed, including treatments of lattice design, compensation schemes, transition crossing, and other radio frequency effects. Operational and diagnostic techniques and the optimization of luminosity are discussed in detail. One chapter is devoted to radiation and the special features of synchrotron light sources. Although the book emphasizes circular machines, much of the treatment applies equally to linear machines and transfer lines.

The Principles of Deep Learning Theory (1版)

類似書籍推薦給您

This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning. Detailed step-by-step explanations for all equations and clear exposition of both old and new concepts in deep learning theory make the book accessible to readers with a minimal prerequisite of linear algebra, calculus, and informal probability theory Many novel results that appear for the first time in the literature, taking readers to the forefront of deep learning theory Provides a unique approach that bridges deep learning and theoretical physics, demonstrating to the ML community how a theoretical physics approach can be useful, while also teaching techniques that are valuable for theoretical physicists