The author suggests that prerequisites include only some calculus and linear algebra. There is a heavy use of open software packages throughout; all the software is available on the Internet. The book is divided into three main parts. The first is an extended introduction to the principles of modeling.
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The second part introduces phenomenological models models based on data , and the third part focuses on mechanistic models mostly models based on differential equations. In the first part of the book the author sets off in that direction. Consider the following selections:. The system and the question relating to the system are indispensable parts of a mathematical model.
The second left me scratching my head; what is he really trying to say? Unfortunately, I found that much of the first part of the book reads like this. Lacking concrete examples, statements like these mean practically nothing to students. The second and third parts of the book are better, but I still found them paradoxically either too wordy or too brief. The first problem is that too many topics are treated with too little detail. Four pages each are given to inferential statistics and neural networks, for example.
Yet when the author does spend time on an example, he is often notably verbose. Nonetheless, there are a number of good examples. A simple example with a temperature sensor is used to investigate both phenomenological and mechanistic models, and to study adequacy and validity of the models. Another good example focuses on the enzyme kinetics of wine fermentation.
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This is a real example of moderate complexity with eighteen parameters that control the process. The author is bursting with enthusiasm for the subject and clearly has broad experience and expertise to share with students. Bill Satzer wjsatzer mmm. His training is in dynamical systems and particularly celestial mechanics; his current interests are broadly in applied mathematics and the teaching of mathematics.
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Harry Potter. Popular Features. New Releases. Description This concise and clear introduction to the topic requires only basic knowledge of calculus and linear algebra - all other concepts and ideas are developed in the course of the book.
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Lucidly written so as to appeal to undergraduates and practitioners alike, it enables readers to set up simple mathematical models on their own and to interpret their results and those of others critically. To achieve this, many examples have been chosen from various fields, such as biology, ecology, economics, medicine, agricultural, chemical, electrical, mechanical and process engineering, which are subsequently discussed in detail.
Based on the author's modeling and simulation experience in science and engineering and as a consultant, the book answers such basic questions as: What is a mathematical model? What types of models do exist? Which model is appropriate for a particular problem? What are simulation, parameter estimation, and validation? The book relies exclusively upon open-source software which is available to everybody free of charge. Auflage Illustrations note 86 SW-Abb.
Back cover copy This concise and clear introduction to the topic requires only basic knowledge of calculus and linear algebra - all other concepts and ideas are developed in the course of the book. Having studied mathematics, physics and economics at the Universities of G?
From , he held a post as project manager and group leader at the Fraunhofer-ITWM in Kaiserslautern consultant projects for the industry. His research emphasizes differential equation models and is documented in 34 scientific publications and one patent. Table of contents Preface 1. Principles of Mathematical Modeling 1.
Mathematical Modeling and Simulation : Introduction for Scientists and Engineers
Phenomenological models 2. Mechanistic models I: ODE's 3.
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Closed form solution 3. Introduction 4.