Algorithms And Computational Theory For Engineering Applications
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Algorithms and Computational Theory for Engineering Applications
This book goes deeply into the world of algorithms and computational theory and its astounding influence on numerous engineering areas. The book's carefully chosen content highlights the most recent studies, approaches, and real-world applications that are revolutionising engineering. The book is structured into distinct sections, each of which examines an important topic in computational theory and algorithms. The authors propose cutting-edge optimisation methods that revolutionise the way engineers approach engineering problems by allowing them to solve complicated issues quickly and effectively. The book illustrates the techniques and equipment used in the fields of data science and big data analytics to glean insightful information from enormous databases. Data visualisation, predictive modelling, clustering, and anomaly detection are a few examples of how algorithms are used to find patterns and trends that help engineers make well-informed decisions. Before being physically implemented, complex systems are built, tested, and optimised in the virtual environment thanks to computational modelling and simulation. The book examines numerical techniques, finite element analysis, computational fluid dynamics, and other simulation techniques to highlight how algorithms are changing engineering system design and performance optimisation. The book also delves into the intriguing field of robotics and control systems. The book's readers will learn about the algorithms that advance sensor fusion, intelligent control, path planning, and real-time systems, paving the way for innovations in autonomous driving, industrial automation, and smart cities. Readers will learn more about how algorithms and computational theory are modifying engineering environments, opening up new opportunities, and changing industries by examining the book's chapters. This book is a must-have for anyone looking to keep on top of the intersection of algorithms, computational theory, and engineering applications because of its concentration on practical applications and theoretical breakthroughs.
Parallel Algorithms in Computational Science and Engineering
This contributed volume highlights two areas of fundamental interest in high-performance computing: core algorithms for important kernels and computationally demanding applications. The first few chapters explore algorithms, numerical techniques, and their parallel formulations for a variety of kernels that arise in applications. The rest of the volume focuses on state-of-the-art applications from diverse domains. By structuring the volume around these two areas, it presents a comprehensive view of the application landscape for high-performance computing, while also enabling readers to develop new applications using the kernels. Readers will learn how to choose the most suitable parallel algorithms for any given application, ensuring that theory and practicality are clearly connected. Applications using these techniques are illustrated in detail, including: Computational materials science and engineering Computational cardiovascular analysis Multiscale analysis of wind turbines and turbomachinery Weather forecasting Machine learning techniques Parallel Algorithms in Computational Science and Engineering will be an ideal reference for applied mathematicians, engineers, computer scientists, and other researchers who utilize high-performance computing in their work.
Emotional Cognitive Neural Algorithms with Engineering Applications
Author: Leonid Perlovsky
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-08-20
Dynamic logic (DL) recently had a highest impact on the development in several areas of modeling and algorithm design. The book discusses classical algorithms used for 30 to 50 years (where improvements are often measured by signal-to-clutter ratio), and also new areas, which did not previously exist. These achievements were recognized by National and International awards. Emerging areas include cognitive, emotional, intelligent systems, data mining, modeling of the mind, higher cognitive functions, evolution of languages and other. Classical areas include detection, recognition, tracking, fusion, prediction, inverse scattering, and financial prediction. All these classical areas are extended to using mixture models, which previously was considered unsolvable in most cases. Recent neuroimaging experiments proved that the brain-mind actually uses DL. „Emotional Cognitive Neural Algorithms with Engineering Applications“ is written for professional scientists and engineers developing computer and information systems, for professors teaching modeling and algorithms, and for students working on Masters and Ph.D. degrees in these areas. The book will be of interest to psychologists and neuroscientists interested in mathematical models of the brain and min das well.