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The study of natural phenomena using computer simulation is a major new research tool in the physical, chemical, biological and social sciences. It is useful for studying simple systems, and it is essential for the study of complex systems. Using Mathematica, an integrated software environment for scientific programming, numerical analysis and visualization, this book describes computer simulations applicable to a wide range of phenomena.
An exploration of the basis for social and economic behaviour. Using cellular automata in particular, the authors model various factors that are involved in a system of individuals who interact socially and economically with one another. Computer simulations in the social sciences provide a laboratory in which qualitative ideas about social and economic interactions can be tested. This brings a new dimension to the science, where 'explanations' abound, but are rarely subject to much experimental testing. The authors have chosen Mathematica because it has a number of features which make it uniquely qualified for use by social scientists, especially those without expertise in computer programming. Further, users can easily access and readily interact with the various 3.0 Mathematica notebooks, plus other data to be found at www.telospub.com.
Introduction to Mathematical Modeling and Computer Simulations is written as a textbook for readers who want to understand the main principles of Modeling and Simulations in settings that are important for the applications, without using the profound mathematical tools required by most advanced texts. It can be particularly useful for applied mathematicians and engineers who are just beginning their careers. The goal of this book is to outline Mathematical Modeling using simple mathematical descriptions, making it accessible for first- and second-year students.
A guide to using Mathematica so as to explore cellular automata within natural phenomena, such as insect colonies, bird flight paths and even DNA sequencing. Designed for physicists, life scientists, and engineers - in fact, everyone dealing with fractals - the book first introduces Mathematica before going on to provide the valuable information needed to properly motivate the code and run the simulations presented in the book. All these simulations have been tested both inside and outside the classroom setting, allowing the book's use as reference material as well as a textbook or course supplement. Packaged together with a DOS diskette enabling cross-platfform access to the code. The files will also be accessible via the World Wide Web.
Based on a highly popular, well-established course taught by the authors, Stochastic Processes: An Introduction, Second Edition discusses the modeling and analysis of random experiments using the theory of probability. It focuses on the way in which the results or outcomes of experiments vary and evolve over time. The text begins with a review of relevant fundamental probability. It then covers several basic gambling problems, random walks, and Markov chains. The authors go on to develop random processes continuous in time, including Poisson, birth and death processes, and general population models. While focusing on queues, they present an extended discussion on the analysis of associated stationary processes. The book also explores reliability and other random processes, such as branching processes, martingales, and a simple epidemic. The appendix contains key mathematical results for reference. Ideal for a one-semester course on stochastic processes, this concise, updated textbook makes the material accessible to students by avoiding specialized applications and instead highlighting simple applications and examples. The associated website contains Mathematica® and R programs that offer flexibility in creating graphs and performing computations.
This book introduces the basic concepts of MathLink programming within Mathematica.
This book revisits many of the problems encountered in introductory quantum mechanics, focusing on computer implementations for finding and visualizing analytical and numerical solutions. It subsequently uses these implementations as building blocks to solve more complex problems, such as coherent laser-driven dynamics in the Rubidium hyperfine structure or the Rashba interaction of an electron moving in 2D. The simulations are highlighted using the programming language Mathematica. No prior knowledge of Mathematica is needed; alternatives, such as Matlab, Python, or Maple, can also be used.
Introduction to Mathematical Modeling and Computer Simulations, Second Edition continues to serve as an engaging and accessible textbook for undergraduates studying mathematical modeling and computer simulations. The book is heavily focussed on applications, and so may have a particular appeal to applied mathematicians, engineers, and others working in applied quantitative disciplines. The book may also be useful as a reference text for reference text for early-career stage practitioners. New to this Edition: A new chapter on Machine Learning and Data Analysis in order to account for recent developments in the field. Chapter 9, ‘Asymptotic Methods in Composites’, has been entirely re-written to make it more consistent with industry and scientific standards. Includes an elementary introduction to programming in Python language. The Jupyter notebooks with examples for Chapter 10 and Appendix A are available for a download from www.Routledge.com/9781032661513.
Using computers to solve problems and model physical problems has fast become an integral part of undergraduate and graduate education in physics. This 3rd year undergraduate and subsequent graduate course is a supplement to courses in theoretical physics and develops problem-solving techniques using the computer. It makes use of the newest version of Mathematica (3.0) while still remaining compatible with older versions The programs using Mathematica 3.0 and C are written for both PCs and workstations, and the problems, source files, and graphic routines help students gain experience from the very beginning.