In this "SpringerBrief" the author considers the underlying problems and questions that are common to numerical models of turbulence in different astrophysical systems. Turbulence has emerged as an important research topic in several areas of astrophysics. Understanding and modeling turbulence is particularly important for the dynamics of the interstellar medium, but also for the intergalactic medium, as well as in stars. The advancement of methods for numerical simulations of astrophysical turbulence, however, is still challenging because of gravity, strong compressibility, magnetic fields, and other effects. The book begins with a review of general aspects of numerical simulations of turbulence. In the main part the author presents findings from his numerical studies on astrophysical turbulence and discusses the astrophysical implications. He also explains in detail the numerical schemes utilized. Readers will find that this book offers a compact yet comprehensive introduction.

This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler's laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks.