Mathematical Aspects of Spin Glasses and Neural Networks by Anton Bovier
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Mathematical Aspects of Spin Glasses and Neural Networks
Mathematical Aspects of Spin Glasses and Neural Networks
Summary
Aimed at graduates and potential researchers, this is a comprehensive introduction to the mathematical aspects of spin glasses and neural networks. It should be useful to mathematicians in probability theory and theoretical physics, and to engineers working in theoretical computer science.
Mathematical Aspects of Spin Glasses and Neural Networks Summary
Table of Contents
1: Statics.- 1.1 Mean Field Models.- Hopfield Models as Generalized Random Mean Field Models.- The Martingale Method for Mean-Field Disordered Systems at High Temperature.- On the Central Limit Theorem for the Overlap in the Hopfield Model.- Limiting Behavior of Random Gibbs Measures: Metastates in Some Disordered Mean Field Models.- On the Storage Capacity of the Hopfield Model.- 1.2 Lattice Models.- Typical Profiles of the Kac-Hopfield Model.- Thermodynamic Chaos and the Structure of Short-Range Spin Glasses.- Random Spin Systems with Long-Range Interactions.- 2: Dynamics.- Langevin Dynamics for Sherrington-Kirkpatrick Spin Glasses.- Sherrington-Kirkpatrick Spin-Glass Dynamics Part II: The Discrete Setting.
Additional information
NPB9780817638634
9780817638634
0817638636
Mathematical Aspects of Spin Glasses and Neural Networks by Anton Bovier
New
Hardback
Birkhauser Boston Inc
1997-12-18
382
N/A
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