Models To Code
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Code-type models for concrete behaviour
Author: fib Fédération internationale du béton
language: en
Publisher: fib Fédération internationale du béton
Release Date: 2013-11-01
fib Model Code 2010 represents the state-of-the-art of code-type models for structural behaviour of concrete. It comprises constitutive relations and material models together with the most important explanatory notes. However the underlying normative work, i.e. the fundamental data as well as the considerations and discussions behind the formulas could not be given within the Model Code text. Based on various experiences gained after the publication of Model Code 1990 this lacking background information will lead in the following to numerous questions arising from Model Code users. Consequently the present bulletin claims to conquer this general weakness of codes in a way to guard against any future misunderstandings of the Model Code 2010 related to its chapter 5.1 (Concrete). It discusses the given formulas in connection with experimental data and the most important international literature. The constitutive relations or material models, being included in MC1990 and forming the basis and point of origin of the Task Group’s work, were critically evaluated, if necessary and possible adjusted, or replaced by completely new approaches. Major criteria have been the physical and thermodynamical soundness as well as practical considerations like simplicity and operationality. This state-of-the-art report is intended for practicizing engineers as well as for researchers and represents a comprehensible summary of the relevant knowledge available to the members of the fib Task Group 8.7 at the time of its drafting. Besides the fact that the bulletin is a background document for Chapter 5.1 of MC2010, it will provide an important foundation for the development of future generations of code-type models related to the characteristics and the behaviour of structural concrete. Further it will offer insights into the complexity of the normative work related to concrete modelling, leading to a better understanding and adequate appreciation of MC2010.
Models to Code
This book teaches you how to translate an executable model of your application to efficient, running code on an embedded microcomputer platform without any mysterious gaps or hidden proprietary tools. There are many benefits to a model-oriented approach to software engineering but the path from models to code is not always clear. Using a pragmatic approach, Models to Code uses annotated model and code examples to illustrate the key principles. You will start off with a brief overview of model based engineering concepts, then quickly dive into two case study Executable UML models that expose the key model elements. You will also understand the future of code translation and approaches you can take with other platforms and languages, as well as open source enhancements and alternative strategies for developers to try. Although the techniques are shown using C and a microcomputer, they are not specific to that language or platform. The code generation strategy you learn can easily be adapted to your own implementation technology. Written by three industry experts, Models to Code is your number one resource for software modelling – add it to your library today. What You Will Learn The purpose and benefits of model driven code generation The specific differences between application and implementation details What details are required in an Executable UML model prior to implementation How to specify an implementation without modifying the application models How to specify an Executable UML model in the pycca scripting language How to specify implementation choices in pycca How a model can be repackaged as an efficient implementation automatically Diverse strategies for converting various model elements into code elements Who This Book Is For This book is for modelers and systems engineers on active MBSE projects (using Executable UML or not), projects using Simulink, Matlab, Dymola, MatrixX and other math modelling tools. Any developers with current or past model experience, professors and students, systems engineers, embedded systems developers, or anyone interested in learning more about software modelling.
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala