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Statistical Mechanics: Algorithms and Computations (Anglais) Broché – 16 novembre 2006

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Descriptions du produit

Revue de presse

This book is the best one I have reviewed all year. (Alan Hinchliffe, Physical Sciences Educational Reviews)

Présentation de l'éditeur

This book discusses the computational approach in modern statistical physics in a clear yet accessible way, and works out its intimate relations with other approaches in theoretical physics. Individual chapters focus on subjects as diverse as the hard sphere liquid, classical spin models, single quantum particles and Bose-Einstein condensation. They contain in-depth discussions of algorithms ranging from basic enumeration methods to modern Monte Carlo techniques. The emphasis is on orientation. Discussions of implementation details are kept to a minimum. The book heavily relies on illustrations, tables and concise printed algorithms to convey key information: all the material remains easily accessible. The book is fully self-contained: graphs and tables can be readily reproduced by programming at most a few dozen lines of computer code. Most sections lead from an elementary discussion to the rich and difficult problems of contemporary computational and statistical physics, and will be of interest to a wide range of students, teachers and researchers in physics and the neighboring sciences. An accompanying CD allows to incorporate the layout material (illustrations, tables, schematic programs) into the reader's own presentations.

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Dans ce livre

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Première phrase
The concept of sampling (obtaining the random positions) is truly complex, and we had better get a grasp of the idea in a simplified setting before applying it in its full power and versatility to the complicated cases of later chapters. Lire la première page
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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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3 internautes sur 3 ont trouvé ce commentaire utile 
Nice, even for a non-physicist 2 avril 2014
Par JJA - Publié sur Amazon.com
Format: Broché Achat vérifié
I wanted to learn some Stat. Mech., so I audited the MOOC taught by this author. The details in the book helped with the MOOC's homework. The two combined did the trick: I learned enough about Stat. Mech. to be satisfied.
1 internautes sur 1 ont trouvé ce commentaire utile 
This is a great introduction to algorithms and computations in Statistical Mechanics 11 avril 2015
Par Sidharth Kshatriya - Publié sur Amazon.com
Format: Broché Achat vérifié
This is a great introduction to algorithms and computations in Statistical Mechanics. The book does not assume a lot of prior knowledge of Physics/Statistical Mechanics which is a great plus. This book is rare because it takes a very strong computational approach towards Physics (unlike most Physics textbooks that approach the subject with a very analytical approach with "nice" functions). The computational approach is really the approach you need to take when doing "real" Physics, in my opinion.

I also find that the book has a rare and sparse elegance too it: Its not needlessly verbose and the author explains his subject without drowning you in words. The approach is not to drown you in equations (either) but teach you just enough so that you understand the Computer Algorithm (which is the primary focus). There are a lot of interesting graphs and diagrams across the book too.

The algorithms explained in this book like the Metropolis algorithm, Simulated Annealing, Random Walks etc. have widespread application in areas outside Physics too. So I would recommend this book to people who find these algorithms abstract to take a look to get a good feel on how these algorithms work in a concrete setting. In other words, even if you are not a physicist, understanding how these algorithms work in physical systems will help in other areas like Machine Learning!

I am almost at the end of a MOOC on Statistical Mechanics taught by the author of this book. The MOOC is amazing (like the book). Ideally it would be amazing to read this book in conjunction with the course but this may not be possible for everyone, of course.

If I would offer a suggestion it would be the author should choose a particular computer language for the algorithms in this book in a future edition. Currently they are in psuedocode. Of course, this way these algorithms are more generic and can be implemented by interested readers in any language. But by implementing them in a particular language they become more "alive". In the MOOC course I'm taking, these algorithms are implemented in Python and there is a sort of instantaneous and interesting quality to them. You can run them, you can play around with them. This is not the case with the psuedocode in the book, sadly.
This book is an excellent introduction suitable for senior level undergraduates and graduates in ... 6 avril 2015
Par A Jolly Good Fellow - Publié sur Amazon.com
Format: Broché
This book is an excellent introduction suitable for senior level undergraduates and graduates in physics interested in statistical mechanics and MCMC simulations. The survey of Hard Disks, Bosons, and Ising Lattices showcases the emergence of entropic forces in random systems and takes readers through the mathematics and history of both computational simulation, and the physics the simulations try to solve. While the book does require some understanding of quantum mechanics through the 3rd and 4th chapters, the footnotes and well-maintained appendices at the end of each chapter keeps readers focused on a "need-to-know" track, while giving options for readers who need supplementary information.
Excellent book 27 mars 2014
Par Guillermo M. Roeder Carbo - Publié sur Amazon.com
Format: Broché Achat vérifié
Very clear in computational concepts of statistical mechanics, I enjoy tihis book, the author shown modern concepts in molecular simulations.
1 internautes sur 3 ont trouvé ce commentaire utile 
I love it, fantastic, a serious book 21 mars 2013
Par Flavio Lichtenstein - Publié sur Amazon.com
Format: Broché Achat vérifié
mix math, physics and algorithms - based on Statiscal Mechanics, presents Physics and Math Theory and how to convert these concepts in algorithms.
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