Solving Computationally Expensive Engineering Problems
DOWNLOAD
Download Solving Computationally Expensive Engineering Problems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Solving Computationally Expensive Engineering Problems 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. If the content not found or just blank you must refresh this page
Solving Computationally Expensive Engineering Problems
DOWNLOAD
Author : Slawomir Koziel
language : en
Publisher: Springer
Release Date : 2014-10-01
Solving Computationally Expensive Engineering Problems written by Slawomir Koziel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-01 with Mathematics categories.
Computational complexity is a serious bottleneck for the design process in virtually any engineering area. While migration from prototyping and experimental-based design validation to verification using computer simulation models is inevitable and has a number of advantages, high computational costs of accurate, high-fidelity simulations can be a major issue that slows down the development of computer-aided design methodologies, particularly those exploiting automated design improvement procedures, e.g., numerical optimization. The continuous increase of available computational resources does not always translate into shortening of the design cycle because of the growing demand for higher accuracy and necessity to simulate larger and more complex systems. Accurate simulation of a single design of a given system may be as long as several hours, days or even weeks, which often makes design automation using conventional methods impractical or even prohibitive. Additional problems include numerical noise often present in the simulation data, possible presence of multiple locally optimum designs, as well as multiple conflicting objectives. In this edited book, various techniques that can alleviate solving computationally expensive engineering design problems are presented. One of the most promising approaches is the use of fast replacement models, so-called surrogates, that reliably represent the expensive, simulation-based model of the system/device of interest but they are much cheaper and analytically tractable. Here, a group of international experts summarize recent developments in the area and demonstrate applications in various disciplines of engineering and science. The main purpose of the work is to provide the basic concepts and formulations of the surrogate-based modeling and optimization paradigm, as well as discuss relevant modeling techniques, optimization algorithms and design procedures. Therefore, this book should be useful to researchers and engineers from any discipline where computationally heavy simulations are used on daily basis in the design process.
Nature Inspired Computation In Engineering
DOWNLOAD
Author : Xin-She Yang
language : en
Publisher: Springer
Release Date : 2016-03-19
Nature Inspired Computation In Engineering written by Xin-She Yang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-19 with Technology & Engineering categories.
This timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.
Surrogate Based Modeling And Optimization
DOWNLOAD
Author : Slawomir Koziel
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-06
Surrogate Based Modeling And Optimization written by Slawomir Koziel and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-06 with Mathematics categories.
Contemporary engineering design is heavily based on computer simulations. Accurate, high-fidelity simulations are used not only for design verification but, even more importantly, to adjust parameters of the system to have it meet given performance requirements. Unfortunately, accurate simulations are often computationally very expensive with evaluation times as long as hours or even days per design, making design automation using conventional methods impractical. These and other problems can be alleviated by the development and employment of so-called surrogates that reliably represent the expensive, simulation-based model of the system or device of interest but they are much more reasonable and analytically tractable. This volume features surrogate-based modeling and optimization techniques, and their applications for solving difficult and computationally expensive engineering design problems. It begins by presenting the basic concepts and formulations of the surrogate-based modeling and optimization paradigm and then discusses relevant modeling techniques, optimization algorithms and design procedures, as well as state-of-the-art developments. The chapters are self-contained with basic concepts and formulations along with applications and examples. The book will be useful to researchers in engineering and mathematics, in particular those who employ computationally heavy simulations in their design work.
Knowledge Incorporation In Evolutionary Computation
DOWNLOAD
Author : Yaochu Jin
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-10-20
Knowledge Incorporation In Evolutionary Computation written by Yaochu Jin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-20 with Mathematics categories.
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in evolution ary computation have been scattered in a wide range of research areas and a systematic handling of this important topic in evolutionary computation still lacks. This edited book is a first attempt to put together the state-of-art and re cent advances on knowledge incorporation in evolutionary computation within a unified framework. Existing methods for knowledge incorporation are di vided into the following five categories according to the functionality of the incorporated knowledge in the evolutionary algorithms. 1. Knowledge incorporation in representation, population initialization, - combination and mutation. 2. Knowledge incorporation in selection and reproduction. 3. Knowledge incorporation in fitness evaluations. 4. Knowledge incorporation through life-time learning and human-computer interactions. 5. Incorporation of human preferences in multi-objective evolutionary com putation. The intended readers of this book are graduate students, researchers and practitioners in all fields of science and engineering who are interested in evolutionary computation. The book is divided into six parts. Part I contains one introductory chapter titled "A selected introduction to evolutionary computation" by Yao, which presents a concise but insightful introduction to evolutionary computation.
Computational Science Iccs 2024
DOWNLOAD
Author : Leonardo Franco
language : en
Publisher: Springer Nature
Release Date : 2024-06-28
Computational Science Iccs 2024 written by Leonardo Franco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-28 with Computers categories.
The 7-volume set LNCS 14832 – 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2–4, 2024. The 155 full papers and 70 short papers included in these proceedings were carefully reviewed and selected from 430 submissions. They were organized in topical sections as follows: Part I: ICCS 2024 Main Track Full Papers; Part II: ICCS 2024 Main Track Full Papers; Part III: ICCS 2024 Main Track Short Papers; Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks and Applications; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Part IV: Biomedical and Bioinformatics Challenges for Computer Science; Computational Health; Part V: Computational Optimization, Modelling, and Simulation; Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine; Machine Learning and Data Assimilation for Dynamical Systems; Multiscale Modelling and Simulation; Part VI: Network Models and Analysis: From Foundations to Artificial Intelligence; Numerical Algorithms and Computer Arithmetic for Computational Science; Quantum Computing; Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks, and Artificial Intelligence; Solving Problems with Uncertainties; Teaching Computational Science
Solution Of Superlarge Problems In Computational Mechanics
DOWNLOAD
Author : James H. Kane
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Solution Of Superlarge Problems In Computational Mechanics written by James H. Kane and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
There is a need to solve problems in solid and fluid mechanics that currently exceed the resources of current and foreseeable supercomputers. The issue revolves around the number of degrees of freedom of simultaneous equations that one needs to accurately describe the problem, and the computer storage and speed limitations which prohibit such solutions. The goals of tHis symposium were to explore some of the latest work being done in both industry and academia to solve such extremely large problems, and to provide a forum for the discussion and prognostication of necessary future direc tions of both man and machine. As evidenced in this proceedings we believe these goals were met. Contained in this volume are discussions of: iterative solvers, and their application to a variety of problems, e.g. structures, fluid dynamics, and structural acoustics; iterative dynamic substructuring and its use in structural acoustics; the use of the boundary element method both alone and in conjunction with the finite element method; the application of finite difference methods to problems of incompressible, turbulent flow; and algorithms amenable to concurrent computations and their applications. Furthermore, discussions of existing computational shortcomings from the big picture point of view are presented that include recommendations for future work.
Simulation Driven Modeling And Optimization
DOWNLOAD
Author : Slawomir Koziel
language : en
Publisher: Springer
Release Date : 2016-02-12
Simulation Driven Modeling And Optimization written by Slawomir Koziel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-12 with Mathematics categories.
This edited volume is devoted to the now-ubiquitous use of computational models across most disciplines of engineering and science, led by a trio of world-renowned researchers in the field. Focused on recent advances of modeling and optimization techniques aimed at handling computationally-expensive engineering problems involving simulation models, this book will be an invaluable resource for specialists (engineers, researchers, graduate students) working in areas as diverse as electrical engineering, mechanical and structural engineering, civil engineering, industrial engineering, hydrodynamics, aerospace engineering, microwave and antenna engineering, ocean science and climate modeling, and the automotive industry, where design processes are heavily based on CPU-heavy computer simulations. Various techniques, such as knowledge-based optimization, adjoint sensitivity techniques, and fast replacement models (to name just a few) are explored in-depth along with an array of the latest techniques to optimize the efficiency of the simulation-driven design process. High-fidelity simulation models allow for accurate evaluations of the devices and systems, which is critical in the design process, especially to avoid costly prototyping stages. Despite this and other advantages, the use of simulation tools in the design process is quite challenging due to associated high computational cost. The steady increase of available computational resources does not always translate into the shortening of the design cycle because of the growing demand for higher accuracy and necessity to simulate larger and more complex systems. For this reason, automated simulation-driven design—while highly desirable—is difficult when using conventional numerical optimization routines which normally require a large number of system simulations, each one already expensive.
Scientific Computing With Matlab
DOWNLOAD
Author : Dingyu Xue
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Scientific Computing With Matlab written by Dingyu Xue and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Mathematics categories.
Scientific Computing with MATLAB®, Second Edition improves students’ ability to tackle mathematical problems. It helps students understand the mathematical background and find reliable and accurate solutions to mathematical problems with the use of MATLAB, avoiding the tedious and complex technical details of mathematics. This edition retains the structure of its predecessor while expanding and updating the content of each chapter. The book bridges the gap between problems and solutions through well-grouped topics and clear MATLAB example scripts and reproducible MATLAB-generated plots. Students can effortlessly experiment with the scripts for a deep, hands-on exploration. Each chapter also includes a set of problems to strengthen understanding of the material.
Transactions On Computational Science Xxxviii
DOWNLOAD
Author : Marina L. Gavrilova
language : en
Publisher: Springer Nature
Release Date : 2021-03-24
Transactions On Computational Science Xxxviii written by Marina L. Gavrilova and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-24 with Computers categories.
The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods. This, the 38th issue of the Transactions on Computational Science, is devoted to research on modelling, optimization, and graphs, with applications in 3D and sketch modelling, engineering design, evolutionary computing, and networks.
Genetic Ai Algorithms Evolutionary Approaches For Solving Complex Computational Problems
DOWNLOAD
Author : Rajesh Ojha Prof (Dr) Ajay Shriram Kushwaha
language : en
Publisher: DeepMisti Publication
Release Date : 2025-01-14
Genetic Ai Algorithms Evolutionary Approaches For Solving Complex Computational Problems written by Rajesh Ojha Prof (Dr) Ajay Shriram Kushwaha and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-14 with Computers categories.
In an era where technology evolves at an unprecedented pace, the demand for efficient, adaptable, and innovative solutions to complex computational problems has never been greater. Traditional algorithms often struggle to tackle the complexity, non-linearity, and scale of challenges faced in various fields, ranging from artificial intelligence (AI) to data science, bioinformatics, and beyond. This is where the power of genetic algorithms (GAs) and other evolutionary computation techniques comes into play, offering a new paradigm for problem-solving inspired by the process of natural selection. Genetic AI Algorithms: Evolutionary Approaches for Solving Complex Computational Problems explores the fascinating intersection of evolutionary biology and computational intelligence. It delves into the principles, techniques, and applications of genetic algorithms (GAs), genetic programming (GP), and other evolutionary strategies to provide readers with a comprehensive understanding of how these methods can be used to address some of the most challenging problems in modern computing. Evolutionary algorithms draw inspiration from the mechanisms of natural evolution, such as selection, mutation, crossover, and inheritance. These methods excel at finding optimal or near-optimal solutions in vast, poorly understood, or highly complex problem spaces. By mimicking the evolutionary process, they can explore potential solutions in ways that are often more robust and flexible than traditional approaches. Whether it’s solving optimization problems, designing neural networks, evolving game strategies, or simulating biological systems, evolutionary algorithms provide a powerful framework for innovation. This book serves as both an introduction and a practical guide for those seeking to harness the power of genetic AI algorithms. It begins with foundational concepts and gradually builds up to more advanced topics, ensuring accessibility for newcomers while providing in-depth insights for experienced practitioners. Through a combination of theory, examples, and case studies, readers will learn how to apply evolutionary algorithms to real-world problems, gain insights into the latest research, and discover new frontiers in computational intelligence. By the end of this journey, readers will be equipped with the knowledge and tools necessary to implement genetic AI algorithms for solving a wide array of complex computational challenges. As you embark on this exploration, I encourage you to think creatively and embrace the potential of evolutionary approaches to drive progress in your work, whether in academia, industry, or any other domain where computational problems abound. In closing, it is my hope that this book inspires further inquiry and discovery in the exciting field of genetic AI algorithms, and that it provides a solid foundation for those seeking to unlock the full potential of evolutionary computation. Authors