Mathematical modeling and simulation: introduction for scientists and engineers
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WILEY-VCH
2009
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| Beschreibung: | XIV, 348 S. Ill., graph. Darst. |
| ISBN: | 9783527407583 3527407588 |
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| 245 | 1 | 0 | |a Mathematical modeling and simulation |b introduction for scientists and engineers |c Kai Velten |
| 264 | 1 | |a Weinheim |b WILEY-VCH |c 2009 | |
| 300 | |a XIV, 348 S. |b Ill., graph. Darst. | ||
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| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Naturwissenschaft | |
| 650 | 4 | |a Computer simulation | |
| 650 | 4 | |a Engineering |x Computer simulation | |
| 650 | 4 | |a Engineering |x Mathematical models | |
| 650 | 4 | |a Mathematical models | |
| 650 | 4 | |a Science |x Computer simulation | |
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KAI VELTEN MATHEMATICAL MODELING AND SIMULATION INTRODUCTION FOR
SCIENTISTS AND ENGINEERS WILEY- VCH WILEY-VCH VERLAG GMBH & CO. KGAA
CONTENTS 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 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 3? 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 MATHEMATICAL MODELING AND
SIMULATION: INTRODUCTION FOR SCIENTISTS AND ENGINEERS. KAI VELTEN
COPYRIGHT 2009 WILEY-VCH VERLAG GMBH & CO. KGAA, WEINHEIM ISBN:
978-3-527-40758-8 VI CONTENTS 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
LINREGEXI. 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 CONTENTS VII 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 N 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 VIII CONTENTS 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 1 70 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
CONTENTS IX 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 O 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 X CONTENTS 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 ID 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.Q 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 A 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 CONTENTS XI
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.I USING R IN A KONSOLE WINDOW 321 B.I.I
BATCH MODE 321 B.1.2 COMMAND MODE 322 B.2 R COMMANDER 322 C MAXIMA 323
C.I USING MAXIMA IN A KONSOLE WINDOW 323 C.I.I BATCH MODE 323 C.1.2
COMMAND MODE 323 C.2 WXMAXIMA 324 REFERENCES 325 INDEX 335 |
| adam_txt |
KAI VELTEN MATHEMATICAL MODELING AND SIMULATION INTRODUCTION FOR
SCIENTISTS AND ENGINEERS WILEY- VCH WILEY-VCH VERLAG GMBH & CO. KGAA
CONTENTS 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 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 3? 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 MATHEMATICAL MODELING AND
SIMULATION: INTRODUCTION FOR SCIENTISTS AND ENGINEERS. KAI VELTEN
COPYRIGHT 2009 WILEY-VCH VERLAG GMBH & CO. KGAA, WEINHEIM ISBN:
978-3-527-40758-8 VI CONTENTS 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
LINREGEXI. 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 CONTENTS VII 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 N 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 VIII CONTENTS 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 1 70 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
CONTENTS IX 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 O 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 X CONTENTS 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 ID 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.Q 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 A 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 CONTENTS XI
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.I USING R IN A KONSOLE WINDOW 321 B.I.I
BATCH MODE 321 B.1.2 COMMAND MODE 322 B.2 R COMMANDER 322 C MAXIMA 323
C.I USING MAXIMA IN A KONSOLE WINDOW 323 C.I.I BATCH MODE 323 C.1.2
COMMAND MODE 323 C.2 WXMAXIMA 324 REFERENCES 325 INDEX 335 |
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| dewey-search | 511.8 |
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| discipline | Informatik Mathematik |
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| genre_facet | Lehrbuch |
| id | DE-604.BV035119708 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:20:55Z |
| indexdate | 2024-07-20T09:53:31Z |
| institution | BVB |
| isbn | 9783527407583 3527407588 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016787397 |
| oclc_num | 299238992 |
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| physical | XIV, 348 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
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| publisher | WILEY-VCH |
| record_format | marc |
| spelling | Velten, Kai Verfasser (DE-588)137007159 aut Mathematical modeling and simulation introduction for scientists and engineers Kai Velten Weinheim WILEY-VCH 2009 XIV, 348 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Ingenieurwissenschaften Mathematisches Modell Naturwissenschaft Computer simulation Engineering Computer simulation Engineering Mathematical models Mathematical models Science Computer simulation Science Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Mathematisches Modell (DE-588)4114528-8 s DE-604 text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3113014&prov=M&dok_var=1&dok_ext=htm Inhaltstext HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016787397&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Velten, Kai Mathematical modeling and simulation introduction for scientists and engineers Ingenieurwissenschaften Mathematisches Modell Naturwissenschaft Computer simulation Engineering Computer simulation Engineering Mathematical models Mathematical models Science Computer simulation Science Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4114528-8 (DE-588)4123623-3 |
| title | Mathematical modeling and simulation introduction for scientists and engineers |
| title_auth | Mathematical modeling and simulation introduction for scientists and engineers |
| title_exact_search | Mathematical modeling and simulation introduction for scientists and engineers |
| title_exact_search_txtP | Mathematical modeling and simulation introduction for scientists and engineers |
| title_full | Mathematical modeling and simulation introduction for scientists and engineers Kai Velten |
| title_fullStr | Mathematical modeling and simulation introduction for scientists and engineers Kai Velten |
| title_full_unstemmed | Mathematical modeling and simulation introduction for scientists and engineers Kai Velten |
| title_short | Mathematical modeling and simulation |
| title_sort | mathematical modeling and simulation introduction for scientists and engineers |
| title_sub | introduction for scientists and engineers |
| topic | Ingenieurwissenschaften Mathematisches Modell Naturwissenschaft Computer simulation Engineering Computer simulation Engineering Mathematical models Mathematical models Science Computer simulation Science Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Ingenieurwissenschaften Mathematisches Modell Naturwissenschaft Computer simulation Engineering Computer simulation Engineering Mathematical models Mathematical models Science Computer simulation Science Mathematical models Lehrbuch |
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