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Constructive Neural Networks For Function Approximation And Their Application To Cfd Shape Optimization
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Book Synopsis Constructive Neural Networks for Function Approximation and Their Application to CFD Shape Optimization by : Adeline Schmitz
Download or read book Constructive Neural Networks for Function Approximation and Their Application to CFD Shape Optimization written by Adeline Schmitz and published by . This book was released on 2007 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Areas of further research are discussed and include investigating other types of network committees as well as modifying the optimizer itself.
Book Synopsis A Constructive Neural Network Algorithm for Function Approximation Using Fit Sigmoids by : Timothy John Draelos
Download or read book A Constructive Neural Network Algorithm for Function Approximation Using Fit Sigmoids written by Timothy John Draelos and published by . This book was released on 1998 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics by : Gianluigi Rozza
Download or read book Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics written by Gianluigi Rozza and published by SIAM. This book was released on 2022-11-21 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reduced order modeling is an important, growing field in computational science and engineering, and this is the first book to address the subject in relation to computational fluid dynamics. It focuses on complex parametrization of shapes for their optimization and includes recent developments in advanced topics such as turbulence, stability of flows, inverse problems, optimization, and flow control, as well as applications. This book will be of interest to researchers and graduate students in the field of reduced order modeling.
Book Synopsis Trends and Applications in Constructive Approximation by : Detlef H. Mache
Download or read book Trends and Applications in Constructive Approximation written by Detlef H. Mache and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains contributions from international experts in the fields of constructive approximation. This area has reached out to encompass the computational and approximation-theoretical aspects of various interesting fields in applied mathematics.
Book Synopsis The Finite Volume Method in Computational Fluid Dynamics by : F. Moukalled
Download or read book The Finite Volume Method in Computational Fluid Dynamics written by F. Moukalled and published by Springer. This book was released on 2015-08-13 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explores both the theoretical foundation of the Finite Volume Method (FVM) and its applications in Computational Fluid Dynamics (CFD). Readers will discover a thorough explanation of the FVM numerics and algorithms used for the simulation of incompressible and compressible fluid flows, along with a detailed examination of the components needed for the development of a collocated unstructured pressure-based CFD solver. Two particular CFD codes are explored. The first is uFVM, a three-dimensional unstructured pressure-based finite volume academic CFD code, implemented within Matlab. The second is OpenFOAM®, an open source framework used in the development of a range of CFD programs for the simulation of industrial scale flow problems. With over 220 figures, numerous examples and more than one hundred exercise on FVM numerics, programming, and applications, this textbook is suitable for use in an introductory course on the FVM, in an advanced course on numerics, and as a reference for CFD programmers and researchers.
Book Synopsis Shape Optimization Problems by : Hideyuki Azegami
Download or read book Shape Optimization Problems written by Hideyuki Azegami and published by Springer Nature. This book was released on 2020-09-30 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides theories on non-parametric shape optimization problems, systematically keeping in mind readers with an engineering background. Non-parametric shape optimization problems are defined as problems of finding the shapes of domains in which boundary value problems of partial differential equations are defined. In these problems, optimum shapes are obtained from an arbitrary form without any geometrical parameters previously assigned. In particular, problems in which the optimum shape is sought by making a hole in domain are called topology optimization problems. Moreover, a problem in which the optimum shape is obtained based on domain variation is referred to as a shape optimization problem of domain variation type, or a shape optimization problem in a limited sense. Software has been developed to solve these problems, and it is being used to seek practical optimum shapes. However, there are no books explaining such theories beginning with their foundations. The structure of the book is shown in the Preface. The theorems are built up using mathematical results. Therefore, a mathematical style is introduced, consisting of definitions and theorems to summarize the key points. This method of expression is advanced as provable facts are clearly shown. If something to be investigated is contained in the framework of mathematics, setting up a theory using theorems prepared by great mathematicians is thought to be an extremely effective approach. However, mathematics attempts to heighten the level of abstraction in order to understand many things in a unified fashion. This characteristic may baffle readers with an engineering background. Hence in this book, an attempt has been made to provide explanations in engineering terms, with examples from mechanics, after accurately denoting the provable facts using definitions and theorems.
Book Synopsis Artificial-Intelligence-based Electrical Machines and Drives by : Peter Vas
Download or read book Artificial-Intelligence-based Electrical Machines and Drives written by Peter Vas and published by Oxford University Press. This book was released on 1999-01-28 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Roughly half of all electricity generated is consumed in motors, and recent efforts to apply artificial intelligence (AI) to improving electric motors are receiving attention worldwide. At present two industrial drives incorporate some form of AI. This book is the first comprehensive discussion of AI applications to electrical machines and drives. It looks at d.c. drives, induction motor drives, synchronous motor drives, switched reluctance motor drives, and sensorless drives. It combines simple explanations of AI-based systems with detailed and unified mathematical and physical treatments, and it includes numerous worked examples, simulations, and experimental results.
Book Synopsis Genetic Algorithms in Search, Optimization, and Machine Learning by : David Edward Goldberg
Download or read book Genetic Algorithms in Search, Optimization, and Machine Learning written by David Edward Goldberg and published by Addison-Wesley Professional. This book was released on 1989 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.
Book Synopsis Neural Networks and Learning Machines by : Simon S. Haykin
Download or read book Neural Networks and Learning Machines written by Simon S. Haykin and published by Prentice Hall. This book was released on 2009 with total page 938 pages. Available in PDF, EPUB and Kindle. Book excerpt: For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Matlab codes used for the computer experiments in the text are available for download at: http: //www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
Book Synopsis An Introduction to Genetic Algorithms for Scientists and Engineers by : David A. Coley
Download or read book An Introduction to Genetic Algorithms for Scientists and Engineers written by David A. Coley and published by World Scientific. This book was released on 1999 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This invaluable book has been designed to be useful to most practising scientists and engineers, whatever their field and however rusty their mathematics and programming might be. The approach taken is largely practical, with algorithms being presented in full and working code (in BASIC, FORTRAN, PASCAL AND C) included on a floppy disk to help the reader get up and running as quickly as possible. The text could also be used as part of an undergraduate course on search and optimisation. Student exercises are included at the end of several of the chapters, many of which are computer-based and designed to encourage exploration of the method.
Book Synopsis Topology Optimization by : Martin Philip Bendsoe
Download or read book Topology Optimization written by Martin Philip Bendsoe and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topology optimization method solves the basic enginee- ring problem of distributing a limited amount of material in a design space. The first edition of this book has become the standard text on optimal design which is concerned with the optimization of structural topology, shape and material. This edition, has been substantially revised and updated to reflect progress made in modelling and computational procedures. It also encompasses a comprehensive and unified description of the state-of-the-art of the so-called material distribution method, based on the use of mathematical programming and finite elements. Applications treated include not only structures but also materials and MEMS.
Book Synopsis Reduced Order Methods for Modeling and Computational Reduction by : Alfio Quarteroni
Download or read book Reduced Order Methods for Modeling and Computational Reduction written by Alfio Quarteroni and published by Springer. This book was released on 2014-06-05 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics. Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.
Book Synopsis The Design and Analysis of Computer Experiments by : Thomas J. Santner
Download or read book The Design and Analysis of Computer Experiments written by Thomas J. Santner and published by Springer. This book was released on 2019-01-08 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes methods for designing and analyzing experiments that are conducted using a computer code, a computer experiment, and, when possible, a physical experiment. Computer experiments continue to increase in popularity as surrogates for and adjuncts to physical experiments. Since the publication of the first edition, there have been many methodological advances and software developments to implement these new methodologies. The computer experiments literature has emphasized the construction of algorithms for various data analysis tasks (design construction, prediction, sensitivity analysis, calibration among others), and the development of web-based repositories of designs for immediate application. While it is written at a level that is accessible to readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers. New to this revised and expanded edition: • An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples • A new comparison of plug-in prediction methodologies for real-valued simulator output • An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions • A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization • A new chapter describing graphical and numerical sensitivity analysis tools • Substantial new material on calibration-based prediction and inference for calibration parameters • Lists of software that can be used to fit models discussed in the book to aid practitioners
Book Synopsis Analytic Methods for Design Practice by : Gyung-Jin Park
Download or read book Analytic Methods for Design Practice written by Gyung-Jin Park and published by Springer Science & Business Media. This book was released on 2007-05-31 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the world of modern engineering, rigorous and definite design methodologies are needed. However, many parts of engineering design are performed in either an ad-hoc manner or based on the intuition of the engineer. This is the first book to look at both stages of the design process – conceptual design and detailed design – and detail design methodologies for every step of the design process. Case studies show how practical design problems can be solved with analytic design methods. This book is an excellent introduction to the subject. The book’s practical focus will make the book useful to practicing engineers as a practical handbook of design.
Book Synopsis Approximation Theory and Numerical Methods by : G. A. Watson
Download or read book Approximation Theory and Numerical Methods written by G. A. Watson and published by John Wiley & Sons. This book was released on 1980 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton
Download or read book Reinforcement Learning, second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Book Synopsis Active Subspaces by : Paul G. Constantine
Download or read book Active Subspaces written by Paul G. Constantine and published by SIAM. This book was released on 2015-03-17 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientists and engineers use computer simulations to study relationships between a model's input parameters and its outputs. However, thorough parameter studies are challenging, if not impossible, when the simulation is expensive and the model has several inputs. To enable studies in these instances, the engineer may attempt to reduce the dimension of the model's input parameter space. Active subspaces are an emerging set of dimension reduction tools that identify important directions in the parameter space. This book describes techniques for discovering a model's active subspace and proposes methods for exploiting the reduced dimension to enable otherwise infeasible parameter studies. Readers will find new ideas for dimension reduction, easy-to-implement algorithms, and several examples of active subspaces in action.
