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Simulation With Python

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Modeling And Simulation In Python

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Author : Allen B. Downey
language : en
Publisher: No Starch Press
Release Date : 2023-05-30

Modeling And Simulation In Python written by Allen B. Downey and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-30 with Computers categories.

Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math. Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions. Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.

Hands On Simulation Modeling With Python

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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-17

Hands On Simulation Modeling With Python written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-17 with Computers categories.

Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key Features 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 Book Description 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. What you will learn 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 Who this book is for 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.

Modeling And Simulation In Python

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AUDIOBOOK

Author : Allen B. Downey
language : en
Publisher: No Starch Press
Release Date : 2023-05-30

Simulation With Python

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Author : Rongpeng Li
language : en
Publisher: Apress
Release Date : 2022-09-07

Simulation With Python written by Rongpeng Li and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-07 with Computers categories.

Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python. What You'll Learn Use Python and numerical computation to demonstrate the power of simulation Choose a paradigm to run a simulation Draw statistical insights from numerical experiments Know how simulation is used to solve real-world problems Who This Book Is For Entry-level to mid-level Python developers from various backgrounds, including backend developers, academic research programmers, data scientists, and machine learning engineers. The book is also useful to high school students and college undergraduates and graduates with STEM backgrounds.

Hands On Simulation Modeling With Python

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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-30

Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook Description Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that'll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you'll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you'll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. 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. What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is for This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.

Modeling And Simulation In Python

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Author : Jason M. Kinser
language : en
Publisher: CRC Press
Release Date : 2022-05-16

Modeling And Simulation In Python written by Jason M. Kinser and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-16 with Computers categories.

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use, and the libraries of tools provides it with efficient versatility. As the tools continue to expand, users can create insightful models and simulations. While the tools offer an easy method to create a pipeline, such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline. Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, virus spread, and Chess. Key Features: Several theories and applications are provided, each with working Python scripts. All Python functions written for this book are archived on GitHub. Readers do not have to be Python experts, but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

Computer Simulation

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Author : Yahya Esmail Osais
language : en
Publisher: CRC Press
Release Date : 2017-11-28

Computer Simulation written by Yahya Esmail Osais and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-28 with Computers categories.

Computer simulation is an effective and popular universal tool that can be applied to almost all disciplines. Requiring only basic knowledge of programming, mathematics, and probability theory, Computer Simulation: A Foundational Approach Using Python takes a hands-on approach to programming to introduce the fundamentals of computer simulation. The main target of the book is computer science and engineering students who are interested mainly in directly applying the techniques to their research problems. The book will be of great interest to senior undergraduate and starting graduate students in the fields of computer science and engineering and industrial engineering.

Introduction To Modeling And Simulation With Matlab And Python

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Author : Steven I. Gordon
language : en
Publisher: CRC Press
Release Date : 2017-07-12

Introduction To Modeling And Simulation With Matlab And Python written by Steven I. Gordon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Computers categories.

Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science, social science, and engineering that wish to learn the principles of computer modeling, as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers, they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise, the authors return to modeling concepts, providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way, the book builds both modeling and programming expertise with a "just-in-time" approach so that by the end of the book, students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading, tutorials, and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition, each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters, the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www.intromodeling.com provides updates to instructions when there are substantial changes in software versions, as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors.

Electromagnetic Simulation Using The Fdtd Method With Python

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Author : Jennifer E. Houle
language : en
Publisher: John Wiley & Sons
Release Date : 2020-01-15

Electromagnetic Simulation Using The Fdtd Method With Python written by Jennifer E. Houle and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-15 with Science categories.

Provides an introduction to the Finite Difference Time Domain method and shows how Python code can be used to implement various simulations This book allows engineering students and practicing engineers to learn the finite-difference time-domain (FDTD) method and properly apply it toward their electromagnetic simulation projects. Each chapter contains a concise explanation of an essential concept and instruction on its implementation into computer code. Included projects increase in complexity, ranging from simulations in free space to propagation in dispersive media. This third edition utilizes the Python programming language, which is becoming the preferred computer language for the engineering and scientific community. Electromagnetic Simulation Using the FDTD Method with Python, Third Edition is written with the goal of enabling readers to learn the FDTD method in a manageable amount of time. Some basic applications of signal processing theory are explained to enhance the effectiveness of FDTD simulation. Topics covered in include one-dimensional simulation with the FDTD method, two-dimensional simulation, and three-dimensional simulation. The book also covers advanced Python features and deep regional hyperthermia treatment planning. Electromagnetic Simulation Using the FDTD Method with Python: Guides the reader from basic programs to complex, three-dimensional programs in a tutorial fashion Includes a rewritten fifth chapter that illustrates the most interesting applications in FDTD and the advanced graphics techniques of Python Covers peripheral topics pertinent to time-domain simulation, such as Z-transforms and the discrete Fourier transform Provides Python simulation programs on an accompanying website An ideal book for senior undergraduate engineering students studying FDTD, Electromagnetic Simulation Using the FDTD Method with Python will also benefit scientists and engineers interested in the subject.

A Student S Guide To Python For Physical Modeling

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Author : Jesse M. Kinder
language : en
Publisher: Princeton University Press
Release Date : 2021-08-03

A Student S Guide To Python For Physical Modeling written by Jesse M. Kinder and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-03 with Science categories.

A fully updated tutorial on the basics of the Python programming language for science students Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This guide introduces a wide range of useful tools, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Animation Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.

Simulation With Python

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