Defocusing nonlinear Schrödinger equations

Defocusing nonlinear Schrödinger equations

by Dodson, Benjamin

Type Book
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This study of Schrödinger equations with power-type nonlinearity provides a great deal of insight into other dispersive partial differential equations and geometric partial differential equations. It presents important proofs, using tools from harmonic analysis, microlocal analysis, functional analysis, and topology. This includes a new proof of Keel-Tao endpoint Strichartz estimates, and a new proof of Bourgain's result for radial, energy-critical NLS. It also provides a detailed presentation …

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Title Statements

  • Full title :Defocusing nonlinear Schrödinger equations / Benjamin Dodson, The Johns Hopkins University.

Summary

This study of Schrödinger equations with power-type nonlinearity provides a great deal of insight into other dispersive partial differential equations and geometric partial differential equations. It presents important proofs, using tools from harmonic analysis, microlocal analysis, functional analysis, and topology. This includes a new proof of Keel-Tao endpoint Strichartz estimates, and a new proof of Bourgain's result for radial, energy-critical NLS. It also provides a detailed presentation of scattering results for energy-critical and mass-critical equations. This book is suitable as the basis for a one-semester course, and serves as a useful introduction to nonlinear Schrödinger equations for those with a background in harmonic analysis, functional analysis, and partial differential equations.

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Edition Statements

  • Publication Date:©2019

Publication Statements

The Physical Object

  • Description:xii, 242 pages ; : 24 cm.

Edition Identifiers

  • ISBN:1108472087, 9781108472081
  • oclc:on1074292311
  • LC Classification:QC174.26.W28
  • Dewey Decimal:530.12/4

Table of Contents

A first look at the mass-critical problem -- The cubic NLS in dimensions three and four -- The energy-critical problem in higher dimensions -- Mass-critical NLS problem in higher dimensions -- Low-dimensional well-posedness results.

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