Download or read book Computational Mechanics with Neural Networks written by Genki Yagawa and published by Springer Nature. This book was released on 2021-02-26 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.
Download or read book Deep Learning in Computational Mechanics written by Stefan Kollmannsberger and published by Springer Nature. This book was released on 2021-08-05 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning’s fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book’s main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature’s evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python.
Download or read book Computational Mechanics with Deep Learning written by Genki Yagawa and published by Springer Nature. This book was released on 2022-10-31 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for students, engineers, and researchers interested in both computational mechanics and deep learning. It presents the mathematical and computational foundations of Deep Learning with detailed mathematical formulas in an easy-to-understand manner. It also discusses various applications of Deep Learning in Computational Mechanics, with detailed explanations of the Computational Mechanics fundamentals selected there. Sample programs are included for the reader to try out in practice. This book is therefore useful for a wide range of readers interested in computational mechanics and deep learning.
Download or read book Machine Learning Low Rank Approximations and Reduced Order Modeling in Computational Mechanics written by Felix Fritzen and published by MDPI. This book was released on 2019-09-18 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.
Download or read book Computational Mechanics for the Twenty first Century written by B. H. V. Topping and published by . This book was released on 2000 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Includes invited lectures presented at The Fifth International Conference on Computational Structures Technology and The Second International Conference on Engineering Computational Technology held in Belgium, September 2000. It includes contributions from: KJ Bathe, JL Chenot, D Chapelle, C Cinquini, M Cross, G De Roeck, and many others.
Download or read book Statistical Mechanics of Neural Networks written by Haiping Huang and published by Springer Nature. This book was released on 2022-01-04 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.
Download or read book Computational Mechanics 95 written by S.N. Atluri and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 3181 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI!, in the earlier conferences (Tokyo, 1986; Atlanta, 1988, Melbourne, 1991; and Hong Kong, 1992) the response to the call for presentations at ICES-95 in Hawaii has been overwhelming. A very careful screening of the extended abstracts resulted in about 500 paper being accepted for presentation. Out of these, written versions of about 480 papers reached the conference secretariat in Atlanta in time for inclusion in these proceedings. The topics covered at ICES-95 range over the broadest spectrum of computational engineering science. The editors thank the international scientific committee, for their advice and encouragement in making ICES-95 a successful scientific event. Special thanks are expressed to the International Association for Boundary Elements Methods for hosting IABEM-95 in conjunction with ICES-95. The editors here express their deepest gratitude to Ms. Stacy Morgan for her careful handling of a myriad of details of ICES-95, often times under severe time constraints. The editors hope that the readers of this proceedings will find a kaleidoscopic view of computational engineering in the year 1995, as practiced in various parts of the world. Satya N. Atluri Atlanta, Georgia, USA Genki Yagawa Tokyo,Japan Thomas A. Cruse Nashville, TN, USA Organizing Committee Professor Genki Yagawa, University of Tokyo, Japan, Chair Professor Satya Atluri, Georgia Institute of Technology, U.S.A.
Download or read book Machine Learning Low Rank Approximations and Reduced Order Modeling in Computational Mechanics written by Felix Fritzen and published by . This book was released on 2019 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.
Download or read book Computational Mechanics written by C. A. Mota Soares and published by . This book was released on 2006-05-22 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Mechanics in solids, structures and coupled problems in engineering is today a mature science with applications to major industrial designs. This book reflects the state of art and it is written by some of the world leading authorities in this field, addressing such topics as: design and topology optimisation, inverse engineering, multibody dynamics, non-linear and railway dynamics, non-linear and textile composites, sandwich structures, uncertainty and reliability of structures, micromechanics of biological materials, computational geometry, multiscale strategies, discrete and mesh free elements, hybrid crack element, adaptive mesh generation, neural networks, structural model validation, vibro-acoustics, active aeroelastic structures, shells with incompressible flows, fluid-structure interaction, aeroelasticity, fluid-saturated and damage porous media and ceramics, high porosity solids, multiphase viscous porous material and masonry. This book contains the edited version of some Plenary and Keynote Lectures presented at the III European Conference on Computational Mechanics: Solids, Structures and Coupled Problems in Engineering (ECCM-2006), held in the National Laboratory of Civil Engineering, Lisbon, Portugal, 5th - 8th June 2006.
Download or read book Statistical Mechanics of Neural Networks written by Luis Garrido and published by . This book was released on 2014-01-15 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Network Modeling written by P. S. Neelakanta and published by CRC Press. This book was released on 2018-02-06 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.
Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Download or read book Computational Structural Mechanics written by Snehashish Chakraverty and published by Academic Press. This book was released on 2018-09-13 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Structural Mechanics: Static and Dynamic Behaviors provides a cutting-edge treatment of functionally graded materials and the computational methods and solutions of FG static and vibration problems of plates. Using the Rayleigh-Ritz method, static and dynamic problems related to behavior of FG rectangular, Levy, elliptic, skew and annular plates are discussed in detail. A thorough review of the latest research results, computational methods and applications of FG technology make this an essential resource for researchers in academia and industry. Explains application-oriented treatments of the functionally graded materials used in industry Addresses relevant algorithms and key computational techniques Provides numerical solutions of static and vibration problems associated with functionally graded beams and plates of different geometries
Download or read book Applications of Computational Mechanics in Geotechnical Engineering written by Matos. M. Fernandes and published by CRC Press. This book was released on 2020-09-10 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third international workshop on applications of computational mechanics in geotechnical engineering discussed the area of computational mechanics applied to geotechnical problems. During the event, topics such as ground reinforcement and computational models were covered.
Download or read book Computational Mechanics written by Zhenhan Yao and published by 清华大学出版社有限公司. This book was released on 2004 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Mechanics written by M. W. Yuan and published by 清华大学出版社有限公司. This book was released on 2004 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Methods for Deep Learning written by Wei Qi Yan and published by Springer Nature. This book was released on 2020-12-04 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.
