Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

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.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Chapter 9: Simulating Physical Phenomena Using Neural Networks

Neural networks are exceptionally effective at getting good characteristics for highly structured data. Physical phenomena are conditioned by numerous variables that can be easily measured through modern sensors. In this way, big data is produced that is difficult to deal with using classic techniques. Neural networks lend themselves to simulating complex environments.

In this chapter, we will learn how to develop models based on artificial neural networks (ANNs) to simulate physical phenomena. We will start by exploring the basic concepts of neural networks, and then we will examine their architecture and main elements. We will demonstrate how to train a network to update its weights. Then, we will apply these concepts to a practical use case to solve a regression problem. In the last part of the chapter, we will analyze deep neural networks.

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