Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide

• 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

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.

• 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

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.

1. Introducing Simulation Models
2. Understanding Randomness and Random Numbers
3. Probability and Data Generating Processes
4. Exploring Monte Carlo Simulations
5. Simulation-Based Markov Decision Process
6. Resampling Methods
7. Using Simulations to Improve and Optimize Systems
8. Using Simulation Models for Financial Engineering
9. Simulating Physical Phenomena Using Neural Networks
10. Modeling and Simulation for Project Management

1. What's Next?

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3. 主要特点
4. 通过实践案例学习如何创建一个真实模型的数字原型
5. 使用仿真建模技术评估你的原型的性能和输出
6. 理解各种统计和物理模拟，利用Python改进系统
7. 书中描述
8. 仿真建模帮助你创建物理模型的数字原型，以分析它们如何工作并预测它们在现实世界中的性能。通过这本综合指南，你将了解使用Python进行的各种计算统计模拟。
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10. 从模拟建模的基础知识开始，你将理解随机性等概念，并探索数据生成过程、重采样方法和自举技术。然后，你将涵盖关键算法，如蒙特卡洛模拟和马尔科夫决策过程，这些算法用于开发数字模拟模型，并发现它们如何被用于解决现实世界的问题。随着你的进步，你将开发仿真模型，以帮助你获得准确的结果并加强决策过程。利用优化技术，你将学会修改模型的性能，以改善结果并使资源得到最佳利用。本书将指导你利用金融工程的实际用例创建一个数字原型，对项目管理进行原型设计以改进规划，并利用神经网络模拟物理现象。
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12. 在本书结束时，你将学会如何构建和部署自己的仿真模型，以克服现实世界的挑战。
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14. 你将学到什么
15. 获得不同类型的仿真模型的概述
16. 掌握随机性和数据生成过程的概念
17. 了解如何处理离散和连续分布的问题
18. 使用蒙特卡洛模拟来计算定积分
19. 了解如何使用马尔科夫链模拟随机漫步
20. 获得人口参数的置信区间和标准误差的稳健估计值
21. 发现如何在现实生活中使用优化方法
22. 运行有效的模拟来分析真实世界的系统
23. 本书适用对象
24. 使用Python进行仿真建模的实践》适用于仿真开发人员和工程师、模型设计者，以及任何已经熟悉用于研究系统行为的基本计算方法的人。本书将帮助你探索高级仿真技术，如蒙特卡洛方法、统计仿真，以及使用Python的更多方法。需要有Python编程语言的工作知识。
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26. 目录
27. 仿真模型介绍
28. 了解随机性和随机数
29. 概率和数据生成过程
30. 探索蒙特卡洛模拟
31. 基于模拟的马尔科夫决策过程
32. 重抽样方法
33. 使用模拟来改进和优化系统
34. 在金融工程中使用仿真模型
35. 使用神经网络模拟物理现象
36. 项目管理的建模和仿真
37. 下一步是什么？