Statistical Physics of Fields

This book is extremely careful in its derivations, which makes line by line careful study very helpful. I really like this. Furthermore, it's able to to cover a very large amount of material in very few pages by being as direct as possible... this might seem mathematical to some, but I think it's great because the math is very clear. Ideally, for a first course, I prefer a systematic approach, leaving the intuitive stuff only when it's justified. Beware, however, the problems are very hard, and often require very good command of the material.

Book arrived in good shape as expected. Happy with purchase. As for the book itself, I got it for a class. It has worked great, but I purchased out of necessity.

Ive always felt that a post-pathria/huang/etc. stat mech book was needed to do a good job on landau theory and perturbation theory for phase transitions...whatever one might call 'statistical field theory.' I found Kardar's lectures (turned into this book) to be exquisitely clear, and everything i'd ever hoped for in the realm of phase transition pedagogy. He has included material beyond the standard m^4 magnetic transition, like the nonlinear sigma model, BKT transition, and random walks. I cannot endorse this book highly enough, and expect it'll become sort of the 'jackson' of stat mech 2.

Amazing book. The lectures (available on the youtube channel for MIT OCW) are also amazing and pair well with reading the book.

Ok, this book will make you work for everything, no free stuff here. Chapters give you only basic information, and you have to work out exercises in the end of the chapter to get the details.

Good book.

Kardar's book is exceptionally good at illustrating the mathematical structure of renormalization and field theory. If you like your physics as rigorous and clean as possible, this is a good book. Of course, it would be easier to read if you have background in QFT, but that's not necessarily a pre-req.Downsides:1. Sometimes Kardar skips the proofs of nontrivial assertions (at least they were nontrivial to me). For example, why does the RG preserve the partition function? Kardar gives a half-sentence outline, but I think it deserves better.2. There are very few worked-out examples in the book, which explains why the exposition portion of the book is only around 200 pages.Both issues can easily be resolved by consulting other sources, so this is fine as a main text. Sometimes, watching Kardar's lectures on OCW can help clear up what he left out in the book. A pleasure to read!

The book is certainly a good book to learn statistical field theory from, but the starry reviews by L. Balents, D. Chandler, E. Stanley, and other NSF fellow travelers are way, way too hyped. No, this book does not compare to Landau's books in any form (or contents, for that matters), and no, you will not be led from the student stage to the expert stage by reading this book alone. The book is just another good graduate level textbook; actually, part of the material can be grasped by a motivated senior undergraduate student interested in stat. mech. (and this is a merit of the good pedagogy included in the presentation of the material).If you want to delve into statistical field theory, I recommend Quantum Field Theory, by K. Huang (at an introductory level similar to that of this book), Statistical Field Theory, by G. Mussardo (at an intermediate level, dealing with complementary subjects), and the monograph Statistical Field Theory by G. Parisi (more advanced, a little bit dated).