Understanding Modelling And Programming
Download Understanding Modelling And Programming PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Understanding Modelling And Programming book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Understanding Modelling and Programming
This book provides a concise overview of modelling and programming by presenting their essential concepts. It enables the reader to better understand the relationships between modelling and programming by describing abstract properties, desired behaviours, intended structures, needed interactions, and other specific viewpoints on the overall system under development. After an introduction to the importance of modelling and programming in the scope of system engineering in chapter 1, the book provides four main chapters covering systems, models, specifications, and programs, each of them with a set of reflection exercises. Chapter 2 explores how systems relate to reality, exploring different perspectives related to the purpose of the system. Chapter 3 explains what it takes to be a model and how models and systems are related and concludes with discussing model semantics, meaning, and correctness. In Chapter 4, specifications are debated which are precise descriptions of models and systems. It presents the language constructs needed to describe systems and shows how the constructs can be expressed in concrete languages, considering both the structure and the behaviour of models. Chapter 5 considers the creation, simulation, and correct execution of specifications (model descriptions or programs). Eventually, Chapter 6 presents a collection of real-world modelling cases. Apart from describing the case, the concepts of the book are applied to the case, thus giving a better understanding of the concepts. The book is carefully designed to explain modelling and programming concepts, their relationships, and their use. Written for computer science students and lecturers, it covers systems, modelling, programming, simulation, and semantics.
Programming Phase-Field Modeling
This textbook provides a fast-track pathway to numerical implementation of phase-field modeling—a relatively new paradigm that has become the method of choice for modeling and simulation of microstructure evolution in materials. It serves as a cookbook for the phase-field method by presenting a collection of codes that act as foundations and templates for developing other models with more complexity. Programming Phase-Field Modeling uses the Matlab/Octave programming package, simpler and more compact than other high-level programming languages, providing ease of use to the widest audience. Particular attention is devoted to the computational efficiency and clarity during development of the codes, which allows the reader to easily make the connection between the mathematical formulism and the numerical implementation of phase-field models. The background materials provided in each case study also provide a forum for undergraduate level modeling-simulations courses as part of their curriculum.
R for Data Science
Author: Hadley Wickham
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-12
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results