Description
Book SynopsisAn important resource that provides an overview of mathematical modelling
Mathematical Modelling offers a comprehensive guide to both analytical and computational aspects of mathematical modelling that encompasses a wide range of subjects. The authors provide an overview of the basic concepts of mathematical modelling and review the relevant topics from differential equations and linear algebra. The text explores the various types of mathematical models, and includes a range of examples that help to describe a variety of techniques from dynamical systems theory.
The book's analytical techniques examine compartmental modelling, stability, bifurcation, discretization, and fixed-point analysis. The theoretical analyses involve systems of ordinary differential equations for deterministic models. The text also contains information on concepts of probability and random variables as the requirements of stochastic processes. In addition, the authors describe algo
Table of Contents
Preface ix
About the CompanionWebsite xi
1 Basic Concepts and Quick Review 1
1.1 Modelling Types 4
1.2 Quick Review 5
1.2.1 First-order Differential Equations 5
1.2.2 Second-order Differential Equations 7
1.2.3 Linear Algebra 9
1.2.4 Scaling 12
Exercises 14
2 Compartmental Modelling 15
2.1 Cascades of Compartments 18
2.2 Parameter Units 23
Exercises 25
3 Analysis Tools 29
3.1 Stability Analysis 32
3.2 Phase-Plane Behavior 37
3.3 Direction Field 47
3.4 Routh–Hurwitz Criterion 49
Exercises 54
4 Bifurcation 59
4.1 Transcritical Bifurcation 60
4.2 Saddle-Node Bifurcation 61
4.3 Pitchfork Bifurcation 62
4.4 Hopf Bifurcation 64
4.5 Solution Types 69
Exercises 73
5 Discretization and Fixed-Point Analysis 77
5.1 Discretization 78
5.1.1 Euler Method 78
5.1.2 Nonstandard Methods 79
5.2 Fixed-Point Analysis 83
Exercises 88
6 Probability and Random Variables 91
6.1 Basic Concepts 91
6.2 Conditional Probabilities 93
6.3 Random Variables 95
6.3.1 Cumulative Distribution Function 95
6.3.2 Discrete Random Variables 96
6.3.3 Continuous Random Variables 98
6.3.4 Waiting Time 101
Exercises 101
7 Stochastic Modelling 103
7.1 Stochastic Processes 103
7.2 Probability Generating Function 108
7.3 Markov Chains 110
7.4 RandomWalks 118
Exercises 119
8 Computer Simulations 125
8.1 Deterministic Structure 125
8.2 Stochastic Structure 128
8.3 Monte Carlo Methods 136
Exercises 138
9 Examples of Mathematical Modelling 143
9.1 Traffic Model 143
9.2 Michaelis–Menten Kinetics 146
9.3 The Brusselator System 148
9.4 Generalized Richards Model 149
9.5 Spruce Budworm Model 152
9.6 FitzHugh–Nagumo Model 153
9.7 Decay Model 155
9.8 The Gambler’s Ruin 158
Exercises 160
References 165
Index 169
