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. 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.
