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Computational Physics: Problem Solving with Python
The classic in the field for more than 25 years, now with more emphasis on data science and machine learning Computational physics combines physics, applied mathematics, and computer science in a cutting-edge multidisciplinary approach to solving realistic physical problems. It has become integral to modern physics research because of its capacity to bridge the gap between mathematical theory and real-world system behavior. Computational Physics provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. Its philosophy is rooted in “learning by doing”, assisted by many sample programs in the popular Python programming language. The first third of the book lays the fundamentals of scientific computing, including programming basics, stable algorithms for differentiation and integration, and matrix computing. The latter two-thirds of the textbook cover more advanced topics such linear and nonlinear differential equations, chaos and fractals, Fourier analysis, nonlinear dynamics, and finite difference and finite elements methods. A particular focus in on the applications of these methods for solving realistic physical problems. Readers of the fourth edition of Computational Physics will also Computational Physics is ideal for students in physics, engineering, materials science, and any subjects drawing on applied physics.
- GenresPhysicsMathematics
592 pages, Paperback
Published April 1, 2024
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Displaying 1 of 1 review
August 22, 2018
I was impressed with this; it does a really nice job of giving one a quick overview of most (certainly not all!) of the computational techniques you want to be aware of if you want to use a computer for a physics calculation.
I think the authors did an especially good job of motivating each technique, making it clear why a particular problem was of physical interest, and why a certain computational approach probably made sense for attempting to solve it.
This book is certainly no Numerical Recipes; it's a lot less comprehensive and detailed. But that's no bad thing. Numerical Recipes is great when you know what you want to do; but it's a bit much for a novice trying to get an overview of the entire field; whereas this text is just right as an overview.
My one complaint (the reason it gets three rather than four stars) is that it is so riddled with typos, maybe one on every page. Many of these were simply irritating, but at least a few were actually material, confusing the presentation and the reader. An unfortunate very poor performance by the editors, which makes the authors look bad, and gives the readers a headache.
I think the authors did an especially good job of motivating each technique, making it clear why a particular problem was of physical interest, and why a certain computational approach probably made sense for attempting to solve it.
This book is certainly no Numerical Recipes; it's a lot less comprehensive and detailed. But that's no bad thing. Numerical Recipes is great when you know what you want to do; but it's a bit much for a novice trying to get an overview of the entire field; whereas this text is just right as an overview.
My one complaint (the reason it gets three rather than four stars) is that it is so riddled with typos, maybe one on every page. Many of these were simply irritating, but at least a few were actually material, confusing the presentation and the reader. An unfortunate very poor performance by the editors, which makes the authors look bad, and gives the readers a headache.
Displaying 1 of 1 review