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

Introducing the basics of neural networks

ANNs are numerical models developed with the aim of reproducing simple neural activities of the human brain, such as object identification and voice recognition. The structure of an ANN is composed of nodes that, similar to the neurons present in a human brain, are interconnected with each other through weighted connections, which reproduce the synapses between neurons.

The system output is updated until it iteratively converges via the connection weights. The information derived from experimental activities is used as input data and the result processed by the network is returned as an output. The input nodes represent the predictive variables, and the output neurons are represented by the dependent variables. We use the predictive variables to process the dependent variables.

ANNs are very versatile in simulating regression and classification problems. They can learn the process of working out the solution to a problem by analyzing...