Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Proper Generalized Decompositions
Download Proper Generalized Decompositions full books in PDF, epub, and Kindle. Read online Proper Generalized Decompositions ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Proper Generalized Decompositions by : Elías Cueto
Download or read book Proper Generalized Decompositions written by Elías Cueto and published by Springer. This book was released on 2016-03-01 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to help researchers overcome the entrance barrier to Proper Generalized Decomposition (PGD), by providing a valuable tool to begin the programming task. Detailed Matlab Codes are included for every chapter in the book, in which the theory previously described is translated into practice. Examples include parametric problems, non-linear model order reduction and real-time simulation, among others. Proper Generalized Decomposition (PGD) is a method for numerical simulation in many fields of applied science and engineering. As a generalization of Proper Orthogonal Decomposition or Principal Component Analysis to an arbitrary number of dimensions, PGD is able to provide the analyst with very accurate solutions for problems defined in high dimensional spaces, parametric problems and even real-time simulation.
Book Synopsis The Proper Generalized Decomposition for Advanced Numerical Simulations by : Francisco Chinesta
Download or read book The Proper Generalized Decomposition for Advanced Numerical Simulations written by Francisco Chinesta and published by . This book was released on 2013-11-30 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis PGD-Based Modeling of Materials, Structures and Processes by : Francisco Chinesta
Download or read book PGD-Based Modeling of Materials, Structures and Processes written by Francisco Chinesta and published by Springer Science & Business. This book was released on 2014-04-23 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the development of a new simulation paradigm allowing for the solution of models that up to now have never been resolved and which result in spectacular CPU time savings (in the order of millions) that, combined with supercomputing, could revolutionize future ICT (information and communication technologies) at the heart of science and technology. The authors have recently proposed a new paradigm for simulation-based engineering sciences called Proper Generalized Decomposition, PGD, which has proved a tremendous potential in many aspects of forming process simulation. In this book a review of the basics of the technique is made, together with different examples of application.
Book Synopsis Separated Representations and PGD-Based Model Reduction by : Francisco Chinesta
Download or read book Separated Representations and PGD-Based Model Reduction written by Francisco Chinesta and published by Springer. This book was released on 2014-09-02 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume start with a description of the construction of reduced models through a review of Proper Orthogonal Decomposition (POD) and reduced basis models, including their mathematical foundations and some challenging applications, then followed by a description of a new generation of simulation strategies based on the use of separated representations (space-parameters, space-time, space-time-parameters, space-space,...), which have led to what is known as Proper Generalized Decomposition (PGD) techniques. The models can be enriched by treating parameters as additional coordinates, leading to fast and inexpensive online calculations based on richer offline parametric solutions. Separated representations are analyzed in detail in the course, from their mathematical foundations to their most spectacular applications. It is also shown how such an approximation could evolve into a new paradigm in computational science, enabling one to circumvent various computational issues in a vast array of applications in engineering science.
Book Synopsis Study of the Proper Generalized Decomposition for Real-time Applications by : Bernat Serra Zueras
Download or read book Study of the Proper Generalized Decomposition for Real-time Applications written by Bernat Serra Zueras and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational simulations are valuable tools for engineers because they facilitate design processes. Complex systems or problems are unreachable by the standard methods (like Finite Element Method, FEM) and Model Order Reduction (MOR) techniques are required. Among those, the Proper Orthogonal Decomposition has proved to be useful in many fields of science and engineering, and especially in solid mechanics. Alternatively, an a priori strategy MOR, the Proper Generalized Decomposition (PGD), is being developed to solve parametric problems in what, in certain areas, are considered "real-time" conditions. The purpose of this study is to introduce the PGD emergent technique and its potential. This approach has been put into context by qualitatively describing MOR techniques when applied in computational solid mechanics, like the also introduced FEM. The PGD key features and advantages, as well as drawbacks, have been introduced in comparison with the POD, a widely extended technique. Regarding the potential of the PGD, its adequacy in augmented reality in computational surgery has already been documented. In this study, the PGD has first been implemented to solve two 2D academic problems successfully (Poisson equation and linear elastics) with full mathematical formulation. Next, based on the linear elastic case study, a real-time application program has been contrived. In the latter, the problem's configuration has been updated, adding more complexity. Throughout this report, the good results obtained with the PGD have been demonstrated. Results show, first, the ability to solve simple cases using a PGD code applied in Matlab and, therefore, validates this approach. Second, it has been proved that it is useful to develop real-time applications, performing as expected. However, there are still open concepts and related issues to improve the methodology. The study has achieved to develop a simple and comfortable approach to the PGD. At the same time, it provides the readers with the basic tools to encourage them to make their first steps in the PGD methodology. With the hope that all this will allow the reader understand the importance and potential of this methodology.
Book Synopsis Decomposition Techniques in Mathematical Programming by : Antonio J. Conejo
Download or read book Decomposition Techniques in Mathematical Programming written by Antonio J. Conejo and published by Springer Science & Business Media. This book was released on 2006-04-28 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization plainly dominates the design, planning, operation, and c- trol of engineering systems. This is a book on optimization that considers particular cases of optimization problems, those with a decomposable str- ture that can be advantageously exploited. Those decomposable optimization problems are ubiquitous in engineering and science applications. The book considers problems with both complicating constraints and complicating va- ables, and analyzes linear and nonlinear problems, with and without in- ger variables. The decomposition techniques analyzed include Dantzig-Wolfe, Benders, Lagrangian relaxation, Augmented Lagrangian decomposition, and others. Heuristic techniques are also considered. Additionally, a comprehensive sensitivity analysis for characterizing the solution of optimization problems is carried out. This material is particularly novel and of high practical interest. This book is built based on many clarifying, illustrative, and compu- tional examples, which facilitate the learning procedure. For the sake of cl- ity, theoretical concepts and computational algorithms are assembled based on these examples. The results are simplicity, clarity, and easy-learning. We feel that this book is needed by the engineering community that has to tackle complex optimization problems, particularly by practitioners and researchersinEngineering,OperationsResearch,andAppliedEconomics.The descriptions of most decomposition techniques are available only in complex and specialized mathematical journals, di?cult to understand by engineers. A book describing a wide range of decomposition techniques, emphasizing problem-solving, and appropriately blending theory and application, was not previously available.
Book Synopsis Advances in Green Energies and Materials Technology by : Younes Chiba
Download or read book Advances in Green Energies and Materials Technology written by Younes Chiba and published by Springer Nature. This book was released on 2021-06-09 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected articles from the Algerian Symposium on Renewable Energy and Materials (ASREM-2020) held at Médéa, Algeria. It highlights the latest advances in the field of green energies and material technology with specific accentuation on numerical plans and recent methodologies designed to solve engineering problems. It includes mathematical models and experimental measurements to study different problems in renewable energy and materials characterization, with contributions from experts in both academia and industry, and presents a platform to further collaborations in this important area.
Book Synopsis Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition by : Haruo Yanai
Download or read book Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition written by Haruo Yanai and published by Springer Science & Business Media. This book was released on 2011-04-06 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space. This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because it is closely related to the former. The book provides systematic and in-depth accounts of these concepts from a unified viewpoint of linear transformations finite dimensional vector spaces. More specially, it shows that projection matrices (projectors) and g-inverse matrices can be defined in various ways so that a vector space is decomposed into a direct-sum of (disjoint) subspaces. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition will be useful for researchers, practitioners, and students in applied mathematics, statistics, engineering, behaviormetrics, and other fields.
Book Synopsis Model Reduction of Parametrized Systems by : Peter Benner
Download or read book Model Reduction of Parametrized Systems written by Peter Benner and published by Springer. This book was released on 2017-09-05 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: The special volume offers a global guide to new concepts and approaches concerning the following topics: reduced basis methods, proper orthogonal decomposition, proper generalized decomposition, approximation theory related to model reduction, learning theory and compressed sensing, stochastic and high-dimensional problems, system-theoretic methods, nonlinear model reduction, reduction of coupled problems/multiphysics, optimization and optimal control, state estimation and control, reduced order models and domain decomposition methods, Krylov-subspace and interpolatory methods, and applications to real industrial and complex problems. The book represents the state of the art in the development of reduced order methods. It contains contributions from internationally respected experts, guaranteeing a wide range of expertise and topics. Further, it reflects an important effor t, carried out over the last 12 years, to build a growing research community in this field. Though not a textbook, some of the chapters can be used as reference materials or lecture notes for classes and tutorials (doctoral schools, master classes).
Book Synopsis Advanced Strategies for the Separated Formulation of Problems in the Proper Generalized Decomposition Framework by : José Vicente Aguado
Download or read book Advanced Strategies for the Separated Formulation of Problems in the Proper Generalized Decomposition Framework written by José Vicente Aguado and published by . This book was released on 2015 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Encyclopedia of Computational Mechanics by : Erwin Stein
Download or read book Encyclopedia of Computational Mechanics written by Erwin Stein and published by . This book was released on 2004 with total page 870 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Computational Mechanics provides a comprehensive collection of knowledge about the theory and practice of computational mechanics.
Book Synopsis Linear Algebra Done Right by : Sheldon Axler
Download or read book Linear Algebra Done Right written by Sheldon Axler and published by Springer Science & Business Media. This book was released on 1997-07-18 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text for a second course in linear algebra, aimed at math majors and graduates, adopts a novel approach by banishing determinants to the end of the book and focusing on understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space has an eigenvalue. The book starts by discussing vector spaces, linear independence, span, basics, and dimension. Students are introduced to inner-product spaces in the first half of the book and shortly thereafter to the finite- dimensional spectral theorem. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition features new chapters on diagonal matrices, on linear functionals and adjoints, and on the spectral theorem; some sections, such as those on self-adjoint and normal operators, have been entirely rewritten; and hundreds of minor improvements have been made throughout the text.
Book Synopsis Generalized Principal Component Analysis by : René Vidal
Download or read book Generalized Principal Component Analysis written by René Vidal and published by Springer. This book was released on 2016-04-11 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
Book Synopsis Numerical Methods for General and Structured Eigenvalue Problems by : Daniel Kressner
Download or read book Numerical Methods for General and Structured Eigenvalue Problems written by Daniel Kressner and published by Springer Science & Business Media. This book was released on 2006-01-20 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about computing eigenvalues, eigenvectors, and invariant subspaces of matrices. Treatment includes generalized and structured eigenvalue problems and all vital aspects of eigenvalue computations. A unique feature is the detailed treatment of structured eigenvalue problems, providing insight on accuracy and efficiency gains to be expected from algorithms that take the structure of a matrix into account.
Book Synopsis Decompositions of Manifolds by : Robert J. Daverman
Download or read book Decompositions of Manifolds written by Robert J. Daverman and published by American Mathematical Soc.. This book was released on with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decomposition theory studies decompositions, or partitions, of manifolds into simple pieces, usually cell-like sets. Since its inception in 1929, the subject has become an important tool in geometric topology. The main goal of the book is to help students interested in geometric topology to bridge the gap between entry-level graduate courses and research at the frontier as well as to demonstrate interrelations of decomposition theory with other parts of geometric topology. With numerous exercises and problems, many of them quite challenging, the book continues to be strongly recommended to everyone who is interested in this subject. The book also contains an extensive bibliography and a useful index of key words, so it can also serve as a reference to a specialist.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
