Preface xiii
1 Principles of Mathematical Modeling 1
1.1 A Complex World Needs Models 1
1.2 Systems, Models, Simulations 3
1.2.1 Teleological Nature of Modeling and Simulation 4
1.2.2 Modeling and Simulation Scheme 4
1.2.3 Simulation 7
1.2.4 System 7
1.2.5 Conceptual and Physical Models 8
1.3 Mathematics as a Natural Modeling Language 9
1.3.1 Input–Output Systems 9
1.3.2 General Form of Experimental Data 10
1.3.3 Distinguished Role of Numerical Data 10
1.4 Definition of Mathematical Models 11
1.5 Examples and Some More Definitions 13
1.5.1 State Variables and System Parameters 15
1.5.2 Using Computer Algebra Software 18
1.5.3 The Problem Solving Scheme 19
1.5.4 Strategies to Set up Simple Models 20
1.5.4.1 Mixture Problem 24
1.5.4.2 Tank Labeling Problem 27
1.5.5 Linear Programming 30
1.5.6 Modeling a Black Box System 31
1.6 Even More Definitions 34
1.6.1 Phenomenological and Mechanistic Models 34
1.6.2 Stationary and Instationary models 38
1.6.3 Distributed and Lumped models 38
1.7 Classification of Mathematical Models 39
1.7.1 From Black to White Box Models 40
1.7.2 SQM Space Classification: S Axis 41
1.7.3 SQM Space Classification: Q Axis 42
1.7.4 SQM Space Classification: M Axis 43
1.8 Everything Looks Like a Nail? 45
2 Phenomenological Models 47
2.1 Elementary Statistics 48
2.1.1 Descriptive Statistics 48
2.1.1.1 Using Calc 49
2.1.1.2 Using the R Commander 51
2.1.2 Random Processes and Probability 52
2.1.2.1 Random Variables 53
2.1.2.2 Probability 53
2.1.2.3 Densities and Distributions 55
2.1.2.4 The Uniform Distribution 57
2.1.2.5 The Normal Distribution 57
2.1.2.6 Expected Value and Standard Deviation 58
2.1.2.7 More on Distributions 60
2.1.3 Inferential Statistics 60
2.1.3.1 Is Crop A’s Yield Really Higher? 61
2.1.3.2 Structure of a Hypothesis Test 61
2.1.3.3 The t test 62
2.1.3.4 Testing Regression Parameters 63
2.1.3.5 Analysis of Variance 63
2.2 Linear Regression 65
2.2.1 The Linear Regression Problem 65
2.2.2 Solution Using Software 66
2.2.3 The Coefficient of Determination 68
2.2.4 Interpretation of the Regression Coefficients 70
2.2.5 Understanding LinRegEx1.r 70
2.2.6 Nonlinear Linear Regression 72
2.3 Multiple Linear Regression 74
2.3.1 The Multiple Linear Regression Problem 74
2.3.2 Solution Using Software 76
2.3.3 Cross-Validation 78
2.4 Nonlinear Regression 80
2.4.1 The Nonlinear Regression Problem 80
2.4.2 Solution Using Software 81
2.4.3 Multiple Nonlinear Regression 83
2.4.4 Implicit and Vector-Valued Problems 86
2.5 Neural Networks 87
2.5.1 General Idea 87
2.5.2 Feed-Forward Neural Networks 89
2.5.3 Solution Using Software 91
2.5.4 Interpretation of the Results 92
2.5.5 Generalization and Overfitting 95
2.5.6 Several Inputs Example 97
2.6 Design of Experiments 99
2.6.1 Completely Randomized Design 100
2.6.2 Randomized Complete Block Design 103
2.6.3 Latin Square and More Advanced Designs 104
2.6.4 Factorial Designs 106
2.6.5 Optimal Sample Size 108
2.7 Other Phenomenological Modeling Approaches 109
2.7.1 Soft Computing 109
2.7.1.1 Fuzzy Model of a Washing Machine 110
2.7.2 Discrete Event Simulation 111
2.7.3 Signal Processing 113
3 Mechanistic Models I: ODEs 117
3.1 Distinguished Role of Differential Equations 117
3.2 Introductory Examples 118
3.2.1 Archaeology Analogy 118
3.2.2 Body Temperature 120
3.2.2.1 Phenomenological Model 120
3.2.2.2 Application 121
3.2.3 Alarm Clock 122
3.2.3.1 Need for a Mechanistic Model 122
3.2.3.2 Applying the Modeling and Simulation Scheme 123
3.2.3.3 Setting Up the Equations 125
3.2.3.4 Comparing Model and Data 126
3.2.3.5 Validation Fails – What Now? 127
3.2.3.6 A Different Way to Explain the Temperature Memory 128
3.2.3.7 Limitations of the Model 129
3.3 General Idea of ODE’s 130
3.3.1 Intrinsic Meaning of π 130
3.3.2 E X Solves An Ode 130
3.3.3 Infinitely Many Degrees of Freedom 131
3.3.4 Intrinsic Meaning of the Exponential Function 132
3.3.5 ODEs as a Function Generator 134
3.4 Setting Up ODE Models 135
3.4.1 Body Temperature Example 135
3.4.1.1 Formulation of an ODE Model 135
3.4.1.2 ODE Reveals the Mechanism 136
3.4.1.3 ODE’s Connect Data and Theory 137
3.4.1.4 Three Ways to Set up ODEs 138
3.4.2 Alarm Clock Example 139
3.4.2.1 A System of Two ODEs 139
3.4.2.2 Parameter Values Based on A priori Information 140
3.4.2.3 Result of a Hand-fit 141
3.4.2.4 A Look into the Black Box 142
3.5 Some Theory You Should Know 143
3.5.1 Basic Concepts 143
3.5.2 First-order ODEs 145
3.5.3 Autonomous, Implicit, and Explicit ODEs 146
3.5.4 The Initial Value Problem 146
3.5.5 Boundary Value Problems 147
3.5.6 Example of Nonuniqueness 149
3.5.7 ODE Systems 150
3.5.8 Linear versus Nonlinear 152
3.6 Solution of ODE’s: Overview 153
3.6.1 Toward the Limits of Your Patience 153
3.6.2 Closed Form versus Numerical Solutions 154
3.7 Closed Form Solutions 156
3.7.1 Right-hand Side Independent of the Independent Variable 156
3.7.1.1 General and Particular Solutions 156
3.7.1.2 Solution by Integration 157
3.7.1.3 Using Computer Algebra Software 158
3.7.1.4 Imposing Initial Conditions 160
3.7.2 Separation of Variables 161
3.7.2.1 Application to the Body Temperature Model 164
3.7.2.2 Solution Using Maxima and Mathematica 165
3.7.3 Variation of Constants 166
3.7.3.1 Application to the Body Temperature Model 167
3.7.3.2 Using Computer Algebra Software 169
3.7.3.3 Application to the Alarm Clock Model 170
3.7.3.4 Interpretation of the Result 171
3.7.4 Dust Particles in the ODE Universe 173
3.8 Numerical Solutions 174
3.8.1 Algorithms 175
3.8.1.1 The Euler Method 175
3.8.1.2 Example Application 176
3.8.1.3 Order of Convergence 178
3.8.1.4 Stiffness 179
3.8.2 Solving ODE’s Using Maxima 180
3.8.2.1 Heuristic Error Control 181
3.8.2.2 ODE Systems 182
3.8.3 Solving ODEs Using R 184
3.8.3.1 Defining the ODE 184
3.8.3.2 Defining Model and Program Control Parameters 186
3.8.3.3 Local Error Control in lsoda 186
3.8.3.4 Effect of the Local Error Tolerances 187
3.8.3.5 A Rule of Thumb to Set the Tolerances 188
3.8.3.6 The Call of lsoda 189
3.8.3.7 Example Applications 190
3.9 Fitting ODE’s to Data 194
3.9.1 Parameter Estimation in the Alarm Clock Model 194
3.9.1.1 Coupling lsoda with nls 195
3.9.1.2 Estimating One Parameter 197
3.9.1.3 Estimating Two Parameters 198
3.9.1.4 Estimating Initial Values 199
3.9.1.5 Sensitivity of the Parameter Estimates 200
3.9.2 The General Parameter Estimation Problem 201
3.9.2.1 One State Variable Characterized by Data 202
3.9.2.2 Several State Variables Characterized by Data 203
3.9.3 Indirect Measurements Using Parameter Estimation 204
3.10 More Examples 205
3.10.1 Predator–Prey Interaction 205
3.10.1.1 Lotka–Volterra Model 205
3.10.1.2 General Dynamical Behavior 207
3.10.1.3 Nondimensionalization 208
3.10.1.4 Phase Plane Plots 209
3.10.2 Wine Fermentation 211
3.10.2.1 Setting Up a Mathematical Model 212
3.10.2.2 Yeast 213
3.10.2.3 Ethanol and Sugar 215
3.10.2.4 Nitrogen 216
3.10.2.5 Using a Hand-fit to Estimate N 0 217
3.10.2.6 Parameter Estimation 219
3.10.2.7 Problems with Nonautonomous Models 220
3.10.2.8 Converting Data into a Function 222
3.10.2.9 Using Weighting Factors 222
3.10.3 Pharmacokinetics 223
3.10.4 Plant Growth 226
4 Mechanistic Models II: PDEs 229
4.1 Introduction 229
4.1.1 Limitations of ODE Models 229
4.1.2 Overview: Strange Animals, Sounds, and Smells 230
4.1.3 Two Problems You Should Be Able to Solve 231
4.2 The Heat Equation 233
4.2.1 Fourier’s Law 234
4.2.2 Conservation of Energy 235
4.2.3 Heat Equation = Fourier’s Law + Energy Conservation 236
4.2.4 Heat Equation in Multidimensions 238
4.2.5 Anisotropic Case 238
4.2.6 Understanding Off-diagonal Conductivities 239
4.3 Some Theory You Should Know 241
4.3.1 Partial Differential Equations 241
4.3.1.1 First-order PDEs 242
4.3.1.2 Second-order PDEs 243
4.3.1.3 Linear versus Nonlinear 243
4.3.1.4 Elliptic, Parabolic, and Hyperbolic Equations 244
4.3.2 Initial and Boundary Conditions 245
4.3.2.1 Well Posedness 246
4.3.2.2 A Rule of Thumb 246
4.3.2.3 Dirichlet and Neumann Conditions 247
4.3.3 Symmetry and Dimensionality 248
4.3.3.1 1D Example 249
4.3.3.2 2D Example 251
4.3.3.3 3D Example 252
4.3.3.4 Rotational Symmetry 252
4.3.3.5 Mirror Symmetry 253
4.3.3.6 Symmetry and Periodic Boundary Conditions 253
4.4 Closed Form Solutions 254
4.4.1 Problem 1 255
4.4.2 Separation of Variables 255
4.4.3 A Particular Solution for Validation 257
4.5 Numerical Solution of PDE’s 257
4.6 The Finite Difference Method 258
4.6.1 Replacing Derivatives with Finite Differences 258
4.6.2 Formulating an Algorithm 259
4.6.3 Implementation in R 260
4.6.4 Error and Stability Issues 262
4.6.5 Explicit and Implicit Schemes 263
4.6.6 Computing Electrostatic Potentials 264
4.6.7 Iterative Methods for the Linear Equations 264
4.6.8 Billions of Unknowns 265
4.7 The Finite-Element Method 266
4.7.1 Weak Formulation of PDEs 267
4.7.2 Approximation of the Weak Formulation 269
4.7.3 Appropriate Choice of the Basis Functions 270
4.7.4 Generalization to Multidimensions 271
4.7.5 Summary of the Main Steps 272
4.8 Finite-element Software 274
4.9 A Sample Session Using Salome-Meca 276
4.9.1 Geometry Definition Step 277
4.9.1.1 Organization of the GUI 277
4.9.1.2 Constructing the Geometrical Primitives 278
4.9.1.3 Excising the Sphere 279
4.9.1.4 Defining the Boundaries 281
4.9.2 Mesh Generation Step 281
4.9.3 Problem Definition and Solution Step 283
4.9.4 Postprocessing Step 285
4.10 A Look Beyond the Heat Equation 286
4.10.1 Diffusion and Convection 288
4.10.2 Flow in Porous Media 290
4.10.2.1 Impregnation Processes 291
4.10.2.2 Two-phase Flow 293
4.10.2.3 Water Retention and Relative Permeability 293
4.10.2.4 Asparagus Drip Irrigation 295
4.10.2.5 Multiphase Flow and Poroelasticity 296
4.10.3 Computational Fluid Dynamics (CFD) 296
4.10.3.1 Navier–Stokes Equations 296
4.10.3.2 Backward Facing Step Problem 298
4.10.3.3 Solution Using Code-Saturne 299
4.10.3.4 Postprocessing Using Salome-Meca 301
4.10.3.5 Coupled Problems 302
4.10.4 Structural Mechanics 303
4.10.4.1 Linear Static Elasticity 303
4.10.4.2 Example: Eye Tonometry 306
4.11 Other Mechanistic Modeling Approaches 309
4.11.1 Difference Equations 309
4.11.2 Cellular Automata 310
4.11.3 Optimal Control Problems 312
4.11.4 Differential-algebraic Problems 314
4.11.5 Inverse Problems 314
A CAELinux and the Book Software 317
B R (Programming Language and Software Environment) 321
B.1 Using R in a Konsole Window 321
B.1.1 Batch Mode 321
B.1.2 Command Mode 322
B.2 R Commander 322
C Maxima 323
C. 1 Using Maxima in a Konsole Window 323
C.1. 1 Batch Mode 323
C.1. 2 Command Mode 323
C. 2 wxMaxima 324
References 325
Index 335
Kai Velten is a professor of mathematics at the University of Applied Sciences, Wiesbaden, Germany, and a modeling and simulation consultant. Having studied mathematics, physics and economics at the Universities of Gottingen and Bonn, he worked at Braunschweig Technical University (Institute of Geoecology, 1990-93) and at Erlangen University (Institute of Applied Mathematics, 1994-95). From 1996-2000, 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.
Very solid introductory text at the undergraduate level aimed at wide audience. Perfectly fits introductory modeling courses at colleges and universities that prefer to use open-source software rather than commercial one, and is an enjoyable reading in the first place. Highly recommended both as a main text and a supplementary one. (...) This delightful book has two unbeatable features that should absolutely win the audience (...) First of all, it illuminates many important conceptual ideas of mathematical modelling (...) Second, (...) this book enthusiastically promotes open-source software that works on most computers and operating systems and is freely available on the web. (...) Professor Velten suggests an elegant approach to mathematical modeling, carefully going through all important steps from identification of a problem, definition of the associated system under study and analysis of the system's properties to design of a mathematical model for the system, its numerical simulation and validation. (Yuri V. Rogovchenko, Zentralblatt MATH, European Mathematical Society) The book is certainly a reference for those, beginners or professional, who search for a complete and easy to follow step-by-step guide in the amazing world of modeling and simulation (...) it is shown that mathematical models and simulation, if adequately used, help to reduce experimental costs by a better exploration of the information content of experimental data (...) it is explained how to analyze a real problem arising from science or engineering and how to best describe it through a mathematical model. A number of examples help the reader to follow step by step the basics of modelling. (Marcello Vasta, Meccanica: International Journal of Theoretical and Applied Mechanics, Vol. 44(3), 2009) The broad subject area covered in this book reflects the background of the author, an experienced mathematical consultant and academic (?) This book differs from almost all other available modeling books in that the author addresses both mechanistic and statistical models as well as ?hybrid? models. Since many problems coming out of industrial and medical applications in recent years require hybrid models, this text is timely. The modeling range is enormous (?) In this single chapter (?Phenomenological Models?) he manages to cover almost all the material one would expect to find in an undergraduate statistics program. (?) Parameter sensitivity and overfitting problems are discussed in a very simple context - very nice! (?) The author points out that, by translating a real-world problem into a mathematical form, one brings to bear on that problem the vast knowledge and powerful and free software tools available within the ?mathematical universe?, and his aim is to enable the reader to source this information. (?) I believe the author has succeeded in providing access to the available tools and an understanding of how to go about using these tools to solve real-world problems. Neville Fowkes (University of Western Australia) in: SIAM Rev. 53(2), 2011, pp. 387-388 (Society of Industrial and Applied Mathematics, Philadelphia, USA)
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