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Hands-On Simulation Modeling with Python

You're reading from Hands-On Simulation Modeling with Python

Product type Book

Published in Jul 2020

Publisher Packt

ISBN-13 9781838985097

Pages 346 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Giuseppe Ciaburro

Table of Contents (16) Chapters

Preface

1. Section 1: Getting Started with Numerical Simulation

2. Chapter 1: Introducing Simulation Models

3. Chapter 2: Understanding Randomness and Random Numbers

4. Chapter 3: Probability and Data Generation Processes

5. Section 2: Simulation Modeling Algorithms and Techniques

6. Chapter 4: Exploring Monte Carlo Simulations

7. Chapter 5: Simulation-Based Markov Decision Processes

8. Chapter 6: Resampling Methods

9. Chapter 7: Using Simulation to Improve and Optimize Systems

10. Section 3: Real-World Applications

11. Chapter 8: Using Simulation Models for Financial Engineering

12. Chapter 9: Simulating Physical Phenomena Using Neural Networks

13. Chapter 10: Modeling and Simulation for Project Management

14. Chapter 11: What's Next?

15. Other Books You May Enjoy

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Discovering the multivariate optimization methods in Python

In this section, we will analyze some numerical optimization methods contained in the Python SciPy library. SciPy is a collection of mathematical algorithms and functions based on NumPy. It contains a series of commands and high-level classes that can be used to manipulate and display data. With SciPy, functionality is added to Python, making it a data processing and system prototyping environment, similar to commercial systems such as MATLAB.

Scientific applications that use SciPy benefit from the development of add-on modules in numerous fields of numerical computing made by developers around the world. Numerical optimization problems are also covered among the available modules.

The SciPy optimize module contains numerous functions for the minimization/maximization of objective functions, both constrained and unconstrained. It treats nonlinear problems with support for both local and global optimization algorithms...

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Authors (1)

Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

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