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Hands-On Simulation Modeling with Python
Hands-On Simulation Modeling with Python

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

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Hands-On Simulation Modeling with Python

Introducing Simulation Models

A simulation model is a tool capable of processing information and data and predicting the responses of a real system to certain inputs, providing effective support for analysis, performance evaluation, and decision-making processes. The term simulation refers to reproducing the behavior of a system. In a simulation, we can use a real model, perhaps in scale, or a numerical model, which reproduces reality using a computer. An example of a real model is a scale model of an airplane, which is then placed in a wind tunnel to run simulated tests to estimate suitable performance measures.

Although, over the years, physicists have developed theoretical laws that we can use to obtain information on the performance of dynamic systems, often, the application of these laws to a real case takes too long. In these cases, it is convenient to construct a numerical simulation model that allows us to simulate the behavior of the system under certain conditions. This elaborated model will allow us to test the functionality of the system in a simple and immediate way, saving considerable resources in terms of time and money.

In this chapter, we’re going to cover the following main topics:

  • Introducing simulation models
  • Classifying simulation models
  • Approaching a simulation-based problem
  • Exploring discrete event simulation
  • Dynamical systems modeling
  • How to run efficient simulations to analyze real-world systems

Technical requirements

In this chapter, an introduction to simulation techniques will be discussed. To deal with the topics at hand, it is necessary that you have a basic knowledge of algebra and mathematical modeling.

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Key benefits

  • Understand various statistical and physical simulations to improve systems using Python
  • Learn to create the numerical prototype of a real model using hands-on examples
  • Evaluate performance and output results based on how the prototype would work in the real world

Description

Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.

Who is this book for?

This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.

What you will learn

  • Get to grips with the concept of randomness and the data generation process
  • Delve into resampling methods
  • Discover how to work with Monte Carlo simulations
  • Utilize simulations to improve or optimize systems
  • Find out how to run efficient simulations to analyze real-world systems
  • Understand how to simulate random walks using Markov chains
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 30, 2022
Length: 460 pages
Edition : 2nd
Language : English
ISBN-13 : 9781804616888
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Length: 460 pages
Edition : 2nd
Language : English
ISBN-13 : 9781804616888
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Table of Contents

18 Chapters
Part 1:Getting Started with Numerical Simulation Chevron down icon Chevron up icon
Chapter 1: Introducing Simulation Models Chevron down icon Chevron up icon
Chapter 2: Understanding Randomness and Random Numbers Chevron down icon Chevron up icon
Chapter 3: Probability and Data Generation Processes Chevron down icon Chevron up icon
Part 2:Simulation Modeling Algorithms and Techniques Chevron down icon Chevron up icon
Chapter 4: Exploring Monte Carlo Simulations Chevron down icon Chevron up icon
Chapter 5: Simulation-Based Markov Decision Processes Chevron down icon Chevron up icon
Chapter 6: Resampling Methods Chevron down icon Chevron up icon
Chapter 7: Using Simulation to Improve and Optimize Systems Chevron down icon Chevron up icon
Chapter 8: Introducing Evolutionary Systems Chevron down icon Chevron up icon
Part 3:Simulation Applications to Solve Real-World Problems Chevron down icon Chevron up icon
Chapter 9: Using Simulation Models for Financial Engineering Chevron down icon Chevron up icon
Chapter 10: Simulating Physical Phenomena Using Neural Networks Chevron down icon Chevron up icon
Chapter 11: Modeling and Simulation for Project Management Chevron down icon Chevron up icon
Chapter 12: Simulating Models for Fault Diagnosis in Dynamic Systems Chevron down icon Chevron up icon
Chapter 13: What’s Next? Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
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4 star 16.7%
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Steven Fernandes Jan 31, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book starts with an introduction to numerical simulation and simulation models. It covers understanding randomness, random numbers, the basics of probability, and data generation processes. Any hands-on book won't be complete without exploring Monte Carlo simulations, Markov decision processes and resampling methods, and evolutionary systems. Finally, the book covers simulation applications to solve real-world problems for financial engineering and physical phenomena using neural networks. The book ends by providing modeling and simulation for project management and fault diagnosis in dynamic systems.
Amazon Verified review Amazon
Devanshu Mar 21, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In this book, the abecedarian concept of running simulations is introduced. Using Matlab, R, and Python is a skill that the author has acquired over the course of 15 years. I enjoyed reading this book and completing the reciprocal coding exercises provided by the content behind each chapter. This title is highly recommended for everyone! Hope it will assist you in achieving what you are looking for in your data wisdom career. I have gained a great deal of experience in understanding different types of simulation styles. Furthermore, the book helps me grasp the generalities and be capable of applying them when needed. Aside from that, it provides a new perspective on what I used to miss out on when I learned Monte Carlo simulation.
Amazon Verified review Amazon
Yiqiao Yin Jan 22, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book introduces the fundamental concept of running simulations. The author has 15+ years of experience using Matlab, R, and Python and he has 2 masters and 1 phd in renowed fields, which is a great title with high honor given. I really enjoyed this book and the complementary coding exercises given by the content behind each chapters. I highly recommend this title to everyone! Hopefully it'll be helpful for you to achieve what you are looking for in your data science career.I have gained a lot of experience in knowing different types of simulation methods. In addition, the book helps me grip the concepts and be able to wield them at my command whenever desired. On top of that, it shed new light of what I used to miss out when I was learning monte carlo simulation.
Amazon Verified review Amazon
gbushnell Jan 27, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a great resource for anyone looking to learn about simulation modeling. It gives a strong foundation around computational statistical simulations, and takes a hands-on approach to implementation. The step-by-step explanations, practical examples, and self-assessment questions make it easy to understand and apply the concepts.The book covers does a great job of covering numerical simulation algorithms, Markov Decision Processes, Monte Carlo methods, bootstrapping. Overall, the book is a great tool for anyone who wants to learn how to build simulation models with Python, and understand the overall fundamentals of simulation modeling. Would highly recommend.
Amazon Verified review Amazon
Dror Mar 09, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In this era of "unreasonable effectiveness of data", computational simulation is becoming ever more important as virtually all engineering fields become more data-driven. Understanding simulation modeling as a general engineering tool is a non-trivial task, however, as most resources available tend to focus on applications in specific and limited domains.This unique book takes a refreshing approach and provides a broad and deep practical overview on simulation modeling as a general computational tool. It begins with covering the fundamentals of numerical simulation, such as random number generation and probability. It then provides a comprehensive description of various simulation modeling and techniques, such as Monte Carlo simulation, Markov decision processes (MDPs), resampling methods, and evolutionary systems. The final part of the book presents a wide range of practical, real-world simulation applications in various domains, such as financial engineering, physics and dynamical systems.In addition, the book takes a very practical, hands-on approach, and the provided code examples (which use the Python programming language) are extremely helpful in demonstrating the implementation of the presented methods and techniques. Note that some basic familiarity with the Python programming language is recommended.This useful guide will benefit any data scientist, physicist, engineer or practitioner looking to leverage computational statistical simulations in their work. Highly recommended!
Amazon Verified review Amazon
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