Introduction To The Theory Of Spin Glasses And Neural Networks, An
eBook - PDF

Introduction To The Theory Of Spin Glasses And Neural Networks, An

  1. 164 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Introduction To The Theory Of Spin Glasses And Neural Networks, An

Book details
Table of contents
Citations

About This Book

This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor. The book begins with a non-mathematical explanation of the problem, and the modern understanding of the physics of the spin-glass state is formulated in general terms. Next, the 'magic' of the replica symmetry breaking scheme is demonstrated and the physics behind it discussed. Recent experiments on real spin-glass materials are briefly described to demonstrate how this somewhat abstract physics can be studied in the laboratory. The final chapters of the book are devoted to statistical models of neural networks.The material here is self-contained and should be accessible to students with a basic knowledge of theoretical physics and statistical mechanics. It has been used for a one-term graduate lecture course at the Landau Institute for Theoretical Physics.

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Yes, you can access Introduction To The Theory Of Spin Glasses And Neural Networks, An by V Dotsenko in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Physics. We have over one million books available in our catalogue for you to explore.

Information

Year
1995
ISBN
9789812798985

Table of contents

  1. Contents
  2. Preface
  3. 1 The Ising Magnetic Systems
  4. 2 Physics of the Spin Glass State
  5. 3 The Replica Method
  6. 4 The Replica Symmetry Breaking
  7. 5 Physics of the Replica Symmetry Breaking
  8. 6 Replica Symmetry Breaking Near Tc
  9. 7 Ultrametricity
  10. 8 Scaling in a Space of Spin-Glass States
  11. 9 Experiments
  12. 10 Partial Annealing
  13. 11 Statistical Models of Neural Networks
  14. 12 The Hopfield model
  15. 13 Partial Annealing in Neural Networks
  16. 14 Other Kinds of the Neural Networks
  17. 15 Technical Appendix
  18. References