Textbook in PDF format

The classic in the field for more than 25 years, now with increased emphasis on Data Science and new chapters on Quantum Computing, Machine Learning (AI), and general relativity.
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.
The codes in this edition of Computational Physics employ the computer language Python. Previous editions have employed Java, Fortran, and C, and used post-computation tools for visualization. Python’s combination of language plus packages now makes it the standard for the explorative and interactive computing that typifies present-day scientific research. Although valuable for research, we have also found Python to be the best language yet for teaching and learning CP. It is free, robust (programs don’t crash), portable (programs run without modifications on various devices), universal (available for most every computer system), has a clean syntax that permits rapid learning, has dynamic typing (changes data types automatically as needed), has high-level, built-in data types (such as complex numbers), and built-in visualization. Furthermore, because Python is interpreted, students can learn the language by executing and analyzing individual statements within an interactive shell, or within a notebook environment, or by running an entire program in one fell swoop. Finally, it is easy to use the myriad of free Python packages supporting numerical algorithms, state-of-the-art visualizations, as well as specialized toolkits that rival those in MatLAB and Mathematica/Maple. And did we mention, all of this is free? Although we do not expect the readers to be programming experts, it is essential to be able to run and modify the sample codes in this book.
Readers of the fourth edition of Computational Physics will also find:
An exceptionally broad range of topics, from simple matrix manipulations to intricate computations in nonlinear dynamics
A whole suite of supplementary material: Python programs, Jupyter notebooks and videos
Computational Physics is ideal for students in physics, engineering, materials science, and any subjects drawing on applied physics