Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process, 2nd Edition

460 Pages·2022·15.727 MB·English

by  Giuseppe Ciaburro

Hands-On Simulation Modeling
with Python
Develop simulation models for improved efficiency and
precision in the decision-making process
Giuseppe Ciaburro
BIRMINGHAM—MUMBAI
Hands-On Simulation Modeling with Python
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First published: July 2020
Second edition: November 2022
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Dedicated to my family, to my sons Luigi and Simone, to my wife Tiziana and to the memory
of my parents.
Contributors
About the author
Giuseppe Ciaburro holds a Ph.D. in environmental technical physics and two master’s degrees in
chemical engineering and acoustic and noise control. He works at the Built Environment Control
Laboratory – Università degli Studi della Campania “Luigi Vanvitelli.” He has over 20 years of work
experience in programming, first in the field of combustion and then in acoustics and noise control.
His core programming knowledge is in Python and R, and he has extensive experience working with
MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional
ITC courses (about 20 years), dealing with e-learning as an author. He has several publications to his
credit: monographs, scientific journals, and thematic conferences. He is currently researching machine
learning applications in acoustics and noise control. He was recently included in the world’s top 2%
scientists list by Stanford University.
I would like to thank the Packt publishing staff who have been very helpful throughout the
preparation of the book.
About the reviewer
Srikanth Sivaramakrishnan is a simulation engineer specializing in the automotive industry with
more than a decade of experience in dynamic simulations, control systems, data analysis, statistical
techniques, and machine learning for use in vehicle dynamics, tire modeling, and control systems. He
has published and has been a technical reviewer for research papers in multiple automotive journals
and for multiple conferences. He currently works as a lead motorsports simulation engineer for
General Motors. He holds an MSc in mechanical engineering from Virginia Polytechnic Institute
and State University, Blacksburg, and a bachelor’s degree in technology from the National Institute
of Technology, Tiruchirappalli, India.

Table of Contents
Preface xv
Part 1:Getting Started with Numerical Simulation
1
Introducing Simulation Models 3
Technical requirements 3 Validation of the simulation model 13
Introducing simulation models 4 Simulation and analysis of results 13
Decision-making workflow 4 Exploring Discrete Event Simulation
Comparing modeling and simulation 5 (DES) 14
Pros and cons of simulation modeling 5 Finite-state machine (FSM) 16
Simulation modeling terminology 6 State transition table (STT) 17
Classifying simulation models 8 State transition graph (STG) 17
Comparing static and dynamic models 8 Dynamic systems modeling 17
Comparing deterministic and stochastic models 8 Managing workshop machinery 17
Comparing continuous and discrete models 9 Simple harmonic oscillator 19
Approaching a simulation-based The predator-prey model 21
problem 9 How to run efficient simulations to
Problem analysis 10 analyze real-world systems 22
Data collection 10 Summary 24
Setting up the simulation model 10
Simulation software selection 11
Verification of the software solution 12
viii Table of Contents
2
Understanding Randomness and Random Numbers 25
Technical requirements 25 Uniformity test 46
Stochastic processes 26 Exploring generic methods for
Types of stochastic processes 27 random distributions 51
Examples of stochastic processes 27 The inverse transform sampling method 52
The Bernoulli process 28 The acceptance-rejection method 53
Random walk 28
Random number generation using
The Poisson process 30
Python 54
Random number simulation 31
Introducing the random module 54
Probability distribution 31 Generating real-value distributions 58
Properties of random numbers 33
Randomness requirements
The pseudorandom number generator 34 for security 59
The pros and cons of a random number Password-based authentication systems 59
generator 34 Random password generator 60
Random number generation algorithms 34
Cryptographic random number
Linear congruential generator 35
generator 62
Random numbers with uniform distribution 37
Introducing cryptography 62
Lagged Fibonacci generator 39
Randomness and cryptography 63
Testing uniform distribution 42
Encrypted/decrypted message generator 64
Chi-squared test 43
Summary 68
3
Probability and Data Generation Processes 69
Technical requirements 69 Exploring probability distributions 76
Explaining probability concepts 70 The probability density function 77
Types of events 70 Mean and variance 78
Calculating probability 71 Uniform distribution 79
Probability definition with an example 71 Binomial distribution 83
Normal distribution 86
Understanding Bayes’ theorem 73
Generating synthetic data 90
Compound probability 74
Bayes’ theorem 75 Real data versus artificial data 90
Table of Contents ix
Synthetic data generation methods 91 Simulation of power analysis 98
The power of a statistical test 98
Data generation with Keras 93
Power analysis 99
Data augmentation 93
Summary 103
Part 2: Simulation Modeling Algorithms and
Techniques
4
Exploring Monte Carlo Simulations 107
Technical requirements 108 Performing numerical integration
Introducing the Monte Carlo using Monte Carlo 122
simulation 108 Defining the problem 122
Monte Carlo components 108 Numerical solution 124
First Monte Carlo application 109 Min-max detection 126
Monte Carlo applications 110 The Monte Carlo method 127
Applying the Monte Carlo method for Pi Visual representation 129
estimation 110
Exploring sensitivity analysis
Understanding the central limit concepts 131
theorem 115 Local and global approaches 132
Law of large numbers 115 Sensitivity analysis methods 133
The central limit theorem 116 Sensitivity analysis in action 133
Applying the Monte Carlo simulation 119 Explaining the cross-entropy method 136
Generating probability distributions 120 Introducing cross-entropy 137
Numerical optimization 120 Cross-entropy in Python 138
Project management 121 Binary cross-entropy as a loss function 140
Summary 142
5
Simulation-Based Markov Decision Processes 143
Technical requirements 144 Overview of Markov processes 146
Introducing agent-based models 144 The agent-environment interface 146