Information Theory and Stochastics for Multiscale Nonlinear SystemsShare this page
Andrew J. Majda; Rafail V. Abramov; Marcus J. Grote
A co-publication of the AMS and Centre de Recherches Mathématiques
This book introduces mathematicians to the fascinating mathematical interplay between ideas from stochastics and information theory and practical issues in studying complex multiscale nonlinear systems. It emphasizes the serendipity between modern applied mathematics and applications where rigorous analysis, the development of qualitative and/or asymptotic models, and numerical modeling all interact to explain complex phenomena.
After a brief introduction to the emerging issues in multiscale modeling, the book has three main chapters. The first chapter is an introduction to information theory with novel applications to statistical mechanics, predictability, and Jupiter's Red Spot for geophysical flows. The second chapter discusses new mathematical issues regarding fluctuation-dissipation theorems for complex nonlinear systems including information flow, various approximations, and illustrates applications to various mathematical models. The third chapter discusses stochastic modeling of complex nonlinear systems. After a general discussion, a new elementary model, motivated by issues in climate dynamics, is utilized to develop a self-contained example of stochastic mode reduction.
Based on A. Majda's Aisenstadt lectures at the University of Montreal, the book is appropriate for both pure and applied mathematics graduate students, postdocs and faculty, as well as interested researchers in other scientific disciplines. No background in geophysical flows is required.
Titles in this series are co-published with the Centre de Recherches Mathématiques.
Table of Contents
Table of Contents
Information Theory and Stochastics for Multiscale Nonlinear Systems
Graduate students and research mathematicians interested in multiscale modeling, information theory, and geophysical flows.