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


In this chapter, we learned how to develop models based on ANNs to simulate physical phenomena. We started by analyzing the basic concepts of neural networks and the principles they are based on that are derived from biological neurons. We examined, in detail, the architecture of an ANN, understanding the concepts of weights, bias, layers, and the activation function.

Subsequently, we analyzed the architecture of a feedforward neural network. We saw how the training of the network with data takes place, and we understood the weight adjustment procedure that leads the network to correctly recognize new observations.

Next, we applied the concepts learned by tackling a practical case. We developed a model based on neural networks to solve a regression problem. We learned how to scale data and then how to subset the data for training and testing. We learned how to develop a model based on linear and MLP regression and how to evaluate the performance of these models to make...