Geometric relativity
Author(s)
Bibliographic Information
Geometric relativity
(Graduate studies in mathematics, v. 201)
American Mathematical Society, c2019
Available at / 28 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
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Note
Includes bibliographical references (p. 343-358) and index
Description and Table of Contents
Description
Many problems in general relativity are essentially geometric in nature, in the sense that they can be understood in terms of Riemannian geometry and partial differential equations. This book is centered around the study of mass in general relativity using the techniques of geometric analysis. Specifically, it provides a comprehensive treatment of the positive mass theorem and closely related results, such as the Penrose inequality, drawing on a variety of tools used in this area of research, including minimal hypersurfaces, conformal geometry, inverse mean curvature flow, conformal flow, spinors and the Dirac operator, marginally outer trapped surfaces, and density theorems. This is the first time these topics have been gathered into a single place and presented with an advanced graduate student audience in mind; several dozen exercises are also included.
The main prerequisite for this book is a working understanding of Riemannian geometry and basic knowledge of elliptic linear partial differential equations, with only minimal prior knowledge of physics required. The second part of the book includes a short crash course on general relativity, which provides background for the study of asymptotically flat initial data sets satisfying the dominant energy condition.
Table of Contents
Riemannian geometry: Scalar curvature
Minimal hypersurfaces
The Riemannian positive mass theorem
The Riemannian Penrose inequality
Spin geometry
Quasi-local mass
Initial data sets: Introduction to general relativity
The spacetime positive mass theorem
Density theorems for the constraint equations
Some facts about second-order linear elliptic operators
Bibliography
Index.
by "Nielsen BookData"