En savoir plus
Table des matières
Approximate Numbers. Basic Programming Techniques. Elements of Scientific Graphics. Sorting and Indexing. Evaluation of Functions. Algebraic and Transcendental Equations. Systems of Linear Equations. Eigenvalue Problems. Modeling of Tabulated Functions. Integration of Functions. Monte Carlo Method. Ordinary Differential Equations. Partial Differential Equations. Appendices. Index.
A propos de l'auteur
Titus Adrian Beu , professor of theoretical and computational physics at the University Babes-Bolyai from Cluj-Napoca, Romania, has been active in the broader field of computational physics for more than 30 years. His research topics have evolved from Tokamak plasma and nuclear reactor calculations in the 1980s, collision theory and molecular cluster spectroscopy in the 1990s, to fullerenes and nanofluidics simulations in recent years. Development of ample computer codes has been at the core of all research projects the author has conducted. In parallel, he has lectured on general programming techniques and advanced numerical methods, general simulation methods, and advanced molecular dynamics.
Résumé
Makes Numerical Programming More Accessible to a Wider Audience
Bearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C plus plus utilizes the author‘s many years of practical research and teaching experience to offer a systematic approach to relevant programming concepts. Adopting a practical, broad appeal, this user-friendly book offers guidance to anyone interested in using numerical programming to solve science and engineering problems. Emphasizing methods generally used in physics and engineering from elementary methods to complex algorithms it gradually incorporates algorithmic elements with increasing complexity.
Develop a Combination of Theoretical Knowledge, Efficient Analysis Skills, and Code Design Know-How
The book encourages algorithmic thinking, which is essential to numerical analysis. Establishing the fundamental numerical methods, application numerical behavior and graphical output needed to foster algorithmic reasoning, coding dexterity, and a scientific programming style, it enables readers to successfully navigate relevant algorithms, understand coding design, and develop efficient programming skills. The book incorporates real code, and includes examples and problem sets to assist in hands-on learning.
Begins with an overview on approximate numbers and programming in Python and C/C plus plus, followed by discussion of basic sorting and indexing methods, as well as portable graphic functionality
Contains methods for function evaluation, solving algebraic and transcendental equations, systems of linear algebraic equations, ordinary differential equations, and eigenvalue problems
Addresses approximation of tabulated functions, regression, integration of one-
