Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide
Key Features
- Learn to create a digital prototype of a real model using hands-on examples
- Evaluate the performance and output of your prototype using simulation modeling techniques
- Understand various statistical and physical simulations to improve systems using Python
Book Description
Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.
Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.
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.
What you will learn
- Gain an overview of the different types of simulation models
- Get to grips with the concepts of randomness and data generation process
- Understand how to work with discrete and continuous distributions
- Work with Monte Carlo simulations to calculate a definite integral
- Find out how to simulate random walks using Markov chains
- Obtain robust estimates of confidence intervals and standard errors of population parameters
- Discover how to use optimization methods in real-life applications
- Run efficient simulations to analyze real-world systems
Who this book is for
Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
Table of Contents
- Introducing Simulation Models
- Understanding Randomness and Random Numbers
- Probability and Data Generating Processes
- Exploring Monte Carlo Simulations
- Simulation-Based Markov Decision Process
- Resampling Methods
- Using Simulations to Improve and Optimize Systems
- Using Simulation Models for Financial Engineering
- Simulating Physical Phenomena Using Neural Networks
- Modeling and Simulation for Project Management
- What's Next?
Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 18 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.
