Computational Physics: With Worked Out Examples in FORTRAN® and MATLAB® (De Gruyter Textbook) - Softcover

Bestehorn, Michael

 
9783110782363: Computational Physics: With Worked Out Examples in FORTRAN® and MATLAB® (De Gruyter Textbook)

Inhaltsangabe

The work shows, by means of examples coming from different corners of physics, how physical and mathematical questions can be answered using a computer. Starting with maps and neural networks, applications from Newton's mechanics described by ordinary differential equations come into the focus, like the computation of planetary orbits or classical molecular dynamics. A large part of the textbook is dedicated to deterministic chaos normally encountered in systems with sufficiently many degrees of freedom. Partial differential equations are studied considering (nonlinear) field theories like quantum mechanics, thermodynamics or fluid mechanics. In the second edition, a new chapter gives a detailed survey on delay or memory systems with a direct application to epidemic and road traffic models. Most of the algorithms are realized in FORTRAN, a language most suitable for effectively solving the discussed problems. On the other hand, the codes given and presented on the book's homepage can be easily translated into other languages. Moreover, several MATLAB examples are presented, mainly for didactic reasons. The book is addressed to advanced Bachelor or Master students of physics, applied mathematics and mechanical engineering.

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Über die Autorin bzw. den Autor

Michael Bestehorn, Brandenburgische Technical University Cottbus, Germany.

Von der hinteren Coverseite

The author presents a broad range of research areas with applications using especially ordinary differential equations (ODE), and partial differential equations (PDE). Fundamental phenomena in classical mechanics and dynamic systems as well as effects in field theories, Monte Carlo methods in statistical physics, and hydrodynamics are discussed with numerous FORTRAN and MATLAB examples. A special chapter is devoted to actual epidemic (Covid-19) and traffic models based on delay-differential equations.

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