Computational Physics

I studied computer science and physics at university, and I'm happy to say this is an excellent text if you're interested in learning about one interesting cross-disciplinary intersection between the two. The book uses Python 3, and most of the sample code uses non-deprecated dependencies (most of the expired packages are in matplotlib).I found it guided me along just well enough so that I could complete most exercises without any high level of hand holding (some of the exercises can be done as mild alterations to the examples). While there are plenty of scientific computing packages out there that will do almost all of the behind-the-scenes heavy lifting for you, this book isn't about using them -- instead, it dives deep into the numerical computing methods often used by those same packages.There's plenty of example programs to step you through the exercises. Also, if you have very little programming experience, the first chapter is committed to getting you caught up to speed, but if I'd have to recommend having at least some experience with programming before tackling this (a single introductory uni course, or some time with self-instruction learning python or some other language should be enough).Topics include Visualization, Accuracy and Speed, Integrals and Derivatives, Solving Linear/Non-Linear Equations, Fourier Transforms, ODEs, PDEs, and Monte Carlo/Random Methods.

This is an incredible book for a first timer. It gives a very good introduction to the Python language and how to apply aspects of it to solving Physics problems.There are a few minor issues with the book (Not worth rating it lower than 5 stars). Dr. Newman will introduce libraries and not explain some of them, such as gaussxw. These are usually libraries and functions on the book website. However, there is no reference to it in the book when introduced. Minor oversite. Another issue is that there are parts of the book where it appears he didn't pay attention to which values he was using.These are types of issues that I am sure will be fixed in later editions. Overall this is an incredible book. I picked up this book to see what it had to say. It has taught me things that I did not know. I love that!

Good content, but my book came damaged

Good Condition

This book is very well written and helpful. There are many examples throughout the book, and explanations are complete yet concise. The progression through topics is intuitive. It is very helpful that the author has a set of online supplemental materials available (eg sample code). Concepts are taught so that they can be applied to other programming languages with ease.

I'm a novice Python programmer, but yet I find this book very accessible, well-structured, clearly written, and comprehensive. I am only about halfway through this book but feel that it has really helped me learn both Python programming and solid numerical analysis skills. My only warning is that without a strong physics background the examples and problems may be a bit opaque, but with a strong physics background this book does an excellent job of connecting the physics with computational techniques.

I am a mathematician with some basic programing experience and I was interested in learning Python mostly for mathematical and educational purposes. I consulted many Python books, but I find myself returning over and over again to Chapters 2 and 3 of this book. If you are new to Python and you are interested in scientific applications this is in my view the best place to start.

1. Easy to read. 2. Good examples. 3. Direct to achieve specific results.

This is an amazing book to learn, from zero, computational physics and python!100% recommended.

This is a very clearly written introduction to computational physics. It includes such modern topics as Fourier Analysis and Random Numbers, which are typically absent from computational physics books, without sacrificing the discussion on traditional topics such as linear algebra and differential equations.The tone of the text is that of a friendly teacher. Very often, computational physics textbook get drowned with unnecessary and dry discussion on algorithms and errors. Newman realises that while such discussion is important it can also unnecessarily repel the new student. Unnecessarily, because we now live in an age where Python and Numpy have made computational physics a joy instead of a chore.Newman embraces the Python programming language. He describes each concept with a small Python implementation. He also discusses library functions available in the Python standard library and in Numpy or Scipy. There is useful discussion on how to use Python and how to install it on your computer.There are not many but very useful and elaborate exercise problems.Do not be worried that this book is self-published. I highly recommend it for college students for their self-study. This book and a laptop with Python installed is a perfect set-up for any student to master computational physics. I also used this book recently in a course I gave to PhD students to good effect.

Excellent balance between keeping things understandable for beginners on this specific field and giving enough depths so that a student with some amount of physics, maths and computing background learns with every page . In the same way, the equations and their derivations, are presented in a very understandable way, without unexplained jumps or comments like "its trivial to see". The presented codes were also very readable and comprehensible.

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Very simple to learn with great examples and problems in physics contexts.