Modeling And Simulation In Python

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Hands-On Simulation Modeling with Python

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
Page : 346 pages
File Size : 50,5 Mb
Release : 2020-07-17
Category : Computers
ISBN : 9781838988654


Hands-On Simulation Modeling with Python Book in PDF, Epub and Kindle

Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key FeaturesLearn to create a digital prototype of a real model using hands-on examplesEvaluate the performance and output of your prototype using simulation modeling techniquesUnderstand various statistical and physical simulations to improve systems using PythonBook 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 learnGain an overview of the different types of simulation modelsGet to grips with the concepts of randomness and data generation processUnderstand how to work with discrete and continuous distributionsWork with Monte Carlo simulations to calculate a definite integralFind out how to simulate random walks using Markov chainsObtain robust estimates of confidence intervals and standard errors of population parametersDiscover how to use optimization methods in real-life applicationsRun efficient simulations to analyze real-world systemsWho 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.

Introduction to Modeling and Simulation with MATLAB® and Python

Author : Steven I. Gordon,Brian Guilfoos
Publisher : CRC Press
Page : 192 pages
File Size : 47,8 Mb
Release : 2017-07-12
Category : Computers
ISBN : 9781498773881


Introduction to Modeling and Simulation with MATLAB® and Python Book in PDF, Epub and Kindle

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 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.

Computer Simulation

Author : Yahya Esmail Osais
Publisher : CRC Press
Page : 275 pages
File Size : 45,6 Mb
Release : 2017-11-28
Category : Computers
ISBN : 9781498726832


Computer Simulation Book in PDF, Epub and Kindle

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.

A Student's Guide to Python for Physical Modeling

Author : Jesse M. Kinder,Philip Nelson
Publisher : Princeton University Press
Page : 168 pages
File Size : 50,9 Mb
Release : 2018-01-30
Category : Computers
ISBN : 9780691180571


A Student's Guide to Python for Physical Modeling Book in PDF, Epub and Kindle

A fully updated tutorial on the basics of the Python programming language for science students Python is a computer programming language that is rapidly gaining popularity throughout the sciences. This fully updated 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 tutorial focuses on fundamentals and introduces a wide range of useful techniques, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Animation Numerous code samples and exercises--with solutions—illustrate new ideas as they are introduced. Web-based resources also accompany this guide and include code samples, data sets, and more. This current edition brings the discussion of the Python language, Spyder development environment, and Anaconda distribution up to date. In addition, a new appendix introduces Jupyter notebooks.

Foundations and Methods of Stochastic Simulation

Author : Barry Nelson
Publisher : Springer Science & Business Media
Page : 276 pages
File Size : 54,7 Mb
Release : 2013-01-31
Category : Business & Economics
ISBN : 9781461461609


Foundations and Methods of Stochastic Simulation Book in PDF, Epub and Kindle

This graduate-level text covers modeling, programming and analysis of simulation experiments and provides a rigorous treatment of the foundations of simulation and why it works. It introduces object-oriented programming for simulation, covers both the probabilistic and statistical basis for simulation in a rigorous but accessible manner (providing all necessary background material); and provides a modern treatment of experiment design and analysis that goes beyond classical statistics. The book emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems and prepares the reader to use simulation in research as well as practice. The book is a rigorous, but concise treatment, emphasizing lasting principles but also providing specific training in modeling, programming and analysis. In addition to teaching readers how to do simulation, it also prepares them to use simulation in their research; no other book does this. An online solutions manual for end of chapter exercises is also be provided.​

Derivatives Analytics with Python

Author : Yves Hilpisch
Publisher : John Wiley & Sons
Page : 376 pages
File Size : 53,8 Mb
Release : 2015-08-03
Category : Business & Economics
ISBN : 9781119037996


Derivatives Analytics with Python Book in PDF, Epub and Kindle

Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.

Pyomo – Optimization Modeling in Python

Author : William E. Hart,Carl Laird,Jean-Paul Watson,David L. Woodruff
Publisher : Springer Science & Business Media
Page : 238 pages
File Size : 55,5 Mb
Release : 2012-02-15
Category : Mathematics
ISBN : 9781461432265


Pyomo – Optimization Modeling in Python Book in PDF, Epub and Kindle

This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

Practical Time Series Analysis

Author : Aileen Nielsen
Publisher : O'Reilly Media
Page : 500 pages
File Size : 42,9 Mb
Release : 2019-09-20
Category : Computers
ISBN : 9781492041627


Practical Time Series Analysis Book in PDF, Epub and Kindle

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance

Introduction to Computational Models with Python

Author : Jose M. Garrido
Publisher : CRC Press
Page : 496 pages
File Size : 41,6 Mb
Release : 2015-08-28
Category : Computers
ISBN : 9781498712040


Introduction to Computational Models with Python Book in PDF, Epub and Kindle

Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy m

Principles of Modeling and Simulation

Author : John A. Sokolowski,Catherine M. Banks
Publisher : John Wiley & Sons
Page : 280 pages
File Size : 50,7 Mb
Release : 2011-09-20
Category : Mathematics
ISBN : 9781118210949


Principles of Modeling and Simulation Book in PDF, Epub and Kindle

Explores wide-ranging applications of modeling and simulationtechniques that allow readers to conduct research and ask "Whatif??" Principles of Modeling and Simulation: A MultidisciplinaryApproach is the first book to provide an introduction tomodeling and simulation techniques across diverse areas of study.Numerous researchers from the fields of social science,engineering, computer science, and business have collaborated onthis work to explore the multifaceted uses of computationalmodeling while illustrating their applications in commonspreadsheets. The book is organized into three succinct parts: Principles of Modeling and Simulation provides a briefhistory of modeling and simulation, outlines its many functions,and explores the advantages and disadvantages of using models inproblem solving. Two major reasons to employ modeling andsimulation are illustrated through the study of a specific problemin conjunction with the use of related applications, thus gaininginsight into complex concepts. Theoretical Underpinnings examines various modelingtechniques and introduces readers to two significant simulationconcepts: discrete event simulation and simulation of continuoussystems. This section details the two primary methods in whichhumans interface with simulations, and it also distinguishes themeaning, importance, and significance of verification andvalidation. Practical Domains delves into specific topics related totransportation, business, medicine, social science, and enterprisedecision support. The challenges of modeling and simulation arediscussed, along with advanced applied principles of modeling andsimulation such as representation techniques, integration into theapplication infrastructure, and emerging technologies. With its accessible style and wealth of real-world examples,Principles of Modeling and Simulation: A MultidisciplinaryApproach is a valuable book for modeling and simulation coursesat the upper-undergraduate and graduate levels. It is also anindispensable reference for researchers and practitioners workingin statistics, mathematics, engineering, computer science,economics, and the social sciences who would like to furtherdevelop their understanding and knowledge of the field.

Bayesian Modeling and Computation in Python

Author : Osvaldo A. Martin,Ravin Kumar,Junpeng Lao
Publisher : CRC Press
Page : 420 pages
File Size : 55,5 Mb
Release : 2021-12-28
Category : Computers
ISBN : 9781000520040


Bayesian Modeling and Computation in Python Book in PDF, Epub and Kindle

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.

Programming for Computations - Python

Author : Svein Linge,Hans Petter Langtangen
Publisher : Springer
Page : 232 pages
File Size : 40,9 Mb
Release : 2016-07-25
Category : Computers
ISBN : 9783319324289


Programming for Computations - Python Book in PDF, Epub and Kindle

This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.

Computational Modeling and Visualization of Physical Systems with Python

Author : Jay Wang
Publisher : Wiley Global Education
Page : 492 pages
File Size : 51,8 Mb
Release : 2016-01-11
Category : Science
ISBN : 9781119179191


Computational Modeling and Visualization of Physical Systems with Python Book in PDF, Epub and Kindle

Computational Modeling, by Jay Wang introduces computational modeling and visualization of physical systems that are commonly found in physics and related areas. The authors begin with a framework that integrates model building, algorithm development, and data visualization for problem solving via scientific computing. Through carefully selected problems, methods, and projects, the reader is guided to learning and discovery by actively doing rather than just knowing physics.

Modeling Creativity

Author : Tom De Smedt
Publisher : University Press Antwerp
Page : 165 pages
File Size : 46,5 Mb
Release : 2013-02-01
Category : Computers
ISBN : 9789057182600


Modeling Creativity Book in PDF, Epub and Kindle

Modeling Creativity (doctoral thesis, 2013) explores how creativity can be represented using computational approaches. Our aim is to construct computer models that exhibit creativity in an artistic context, that is, that are capable of generating or evaluating an artwork (visual or linguistic), an interesting new idea, a subjective opinion. The research was conducted in 2008–2012 at the Computational Linguistics Research Group (CLiPS, University of Antwerp) under the supervision of Prof. Walter Daelemans. Prior research was also conducted at the Experimental Media Research Group (EMRG, St. Lucas University College of Art & Design Antwerp) under the supervision of Lucas Nijs. Modeling Creativity examines creativity in a number of different perspectives: from its origins in nature, which is essentially blind, to humans and machines, and from generating creative ideas to evaluating and learning their novelty and usefulness. We will use a hands-on approach with case studies and examples in the Python programming language.

Model Engineering for Simulation

Author : Lin Zhang,Bernard P. Zeigler,Yuanjun LaiLi
Publisher : Academic Press
Page : 453 pages
File Size : 43,9 Mb
Release : 2019-02-27
Category : Mathematics
ISBN : 9780128135440


Model Engineering for Simulation Book in PDF, Epub and Kindle

Model Engineering for Simulation provides a systematic introduction to the implementation of generic, normalized and quantifiable modeling and simulation using DEVS formalism. It describes key technologies relating to model lifecycle management, including model description languages, complexity analysis, model management, service-oriented model composition, quantitative measurement of model credibility, and model validation and verification. The book clearly demonstrates how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. Guides systems and control engineers in the practical creation and delivery of simulation models using DEVS formalism Provides practical methods to improve credibility of models and manage the model lifecycle Helps readers gain an overall understanding of model lifecycle management and analysis Supported by an online ancillary package that includes an instructors and student solutions manual