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Deep Learning For Fluid Simulation And Animation
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Book Synopsis Deep Learning for Fluid Simulation and Animation by : Gilson Antonio Giraldi
Download or read book Deep Learning for Fluid Simulation and Animation written by Gilson Antonio Giraldi and published by Springer Nature. This book was released on 2023 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods – and at a lower computational cost. This work starts with a brief review of computability theory, aimed to convince the reader – more specifically, researchers of more traditional areas of mathematical modeling – about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed. The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches.
Book Synopsis The Art of Fluid Animation by : Jos Stam
Download or read book The Art of Fluid Animation written by Jos Stam and published by CRC Press. This book was released on 2015-11-04 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fluid simulation is a computer graphic used to develop realistic animation of liquids in modern games. The Art of Fluid Animation describes visually rich techniques for creating fluid-like animations that do not require advanced physics or mathematical skills. It explains how to create fluid animations like water, smoke, fire, and explosions throug
Book Synopsis Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research by : Jun Yu
Download or read book Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research written by Jun Yu and published by John Wiley & Sons. This book was released on 2013-03-18 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations
Book Synopsis Fluid Simulation for Computer Graphics by : Robert Bridson
Download or read book Fluid Simulation for Computer Graphics written by Robert Bridson and published by CRC Press. This book was released on 2015-09-18 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical introduction, the second edition of Fluid Simulation for Computer Graphics shows you how to animate fully three-dimensional incompressible flow. It covers all the aspects of fluid simulation, from the mathematics and algorithms to implementation, while making revisions and updates to reflect changes in the field since the first edition. Highlights of the Second Edition New chapters on level sets and vortex methods Emphasizes hybrid particle–voxel methods, now the industry standard approach Covers the latest algorithms and techniques, including: fluid surface reconstruction from particles; accurate, viscous free surfaces for buckling, coiling, and rotating liquids; and enhanced turbulence for smoke animation Adds new discussions on meshing, particles, and vortex methods The book changes the order of topics as they appeared in the first edition to make more sense when reading the first time through. It also contains several updates by distilling author Robert Bridson’s experience in the visual effects industry to highlight the most important points in fluid simulation. It gives you an understanding of how the components of fluid simulation work as well as the tools for creating your own animations.
Book Synopsis Knowledge Guided Machine Learning by : Anuj Karpatne
Download or read book Knowledge Guided Machine Learning written by Anuj Karpatne and published by CRC Press. This book was released on 2022-08-15 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Book Synopsis Computer Animation and Social Agents by : Feng Tian
Download or read book Computer Animation and Social Agents written by Feng Tian and published by Springer Nature. This book was released on 2020-11-25 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the 33rd International Conference on Computer Animation and Social Agents, CASA 2020, held in Bournemouth, UK*, in October 2020. The 1 full paper and 13 short papers presented were carefully reviewed and selected from a total of 86 submissions. The papers are organized in topical sections of modelling, animation and simulation; virtual reality; image processing and computer vision. *The conference was held virtually due to the COVID-19 pandemic.
Book Synopsis Recent Advances in Big Data and Deep Learning by : Luca Oneto
Download or read book Recent Advances in Big Data and Deep Learning written by Luca Oneto and published by Springer. This book was released on 2019-04-02 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.
Book Synopsis Computational Mechanics with Neural Networks by : Genki Yagawa
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.
Book Synopsis Advances in Computer Graphics by : Nadia Magnenat-Thalmann
Download or read book Advances in Computer Graphics written by Nadia Magnenat-Thalmann and published by Springer Nature. This book was released on 2023-01-01 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 39th Computer Graphics International Conference on Advances in Computer Graphics, CGI 2022, held Virtually, during September 12–16, 2022. The 45 full papers included in this book were carefully reviewed and selected from 139 submissions. They were organized in topical sections as follows: image analysis & processing; graphs & networks; estimation & feature matching; 3d reconstruction; rendering & animation; detection & recognition; colors, paintings & layout; synthesis & generation; ar & user interfaces; medical imaging; segmentation; object detection; image attention & perception; and modeling & simulation.
Book Synopsis Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research by : Jun Yu
Download or read book Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research written by Jun Yu and published by John Wiley & Sons. This book was released on 2013-03-27 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations
Book Synopsis Fluid Engine Development by : Doyub Kim
Download or read book Fluid Engine Development written by Doyub Kim and published by CRC Press. This book was released on 2017-01-20 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the splash of breaking waves to turbulent swirling smoke, the mathematical dynamics of fluids are varied and continue to be one of the most challenging aspects in animation. Fluid Engine Development demonstrates how to create a working fluid engine through the use of particles and grids, and even a combination of the two. Core algorithms are explained from a developer’s perspective in a practical, approachable way that will not overwhelm readers. The Code Repository offers further opportunity for growth and discussion with continuously changing content and source codes. This book helps to serve as the ultimate guide to navigating complex fluid animation and development.
Book Synopsis Engineering Turbulence Modelling and Experiments - 4 by : D. Laurence
Download or read book Engineering Turbulence Modelling and Experiments - 4 written by D. Laurence and published by Elsevier. This book was released on 1999-04-14 with total page 972 pages. Available in PDF, EPUB and Kindle. Book excerpt: These proceedings contain the papers presented at the 4th International Symposium on Engineering Turbulence Modelling and Measurements held at Ajaccio, Corsica, France from 24-26 May 1999. It follows three previous conferences on the topic of engineering turbulence modelling and measurements. The purpose of this series of symposia is to provide a forum for presenting and discussing new developments in the area of turbulence modelling and measurements, with particular emphasis on engineering-related problems. Turbulence is still one of the key issues in tackling engineering flow problems. As powerful computers and accurate numerical methods are now available for solving the flow equations, and since engineering applications nearly always involve turbulence effects, the reliability of CFD analysis depends more and more on the performance of the turbulence models. Successful simulation of turbulence requires the understanding of the complex physical phenomena involved and suitable models for describing the turbulent momentum, heat and mass transfer. For the understanding of turbulence phenomena, experiments are indispensable, but they are equally important for providing data for the development and testing of turbulence models and hence for CFD software validation.
Book Synopsis Data-driven modeling and optimization in fluid dynamics: From physics-based to machine learning approaches by : Michel Bergmann
Download or read book Data-driven modeling and optimization in fluid dynamics: From physics-based to machine learning approaches written by Michel Bergmann and published by Frontiers Media SA. This book was released on 2023-01-05 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Learning in Gaming and Animations by : Vikas Chaudhary
Download or read book Deep Learning in Gaming and Animations written by Vikas Chaudhary and published by CRC Press. This book was released on 2021-12 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges. This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking the reader through the fundamental ideas with expert ease. The book progresses on the topics in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence, deep learning, and machine learning have changed the world in gaming and animation. It gives us a motivation that AI can also be applied in gaming, and there are limited textbooks in this area. The book we will write will comprehensively address all the aspects of AI & Deep Learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Our book Deep Learning in Gaming and Animation teaches you how to apply the power of deep learning to build complex reasoning tasks. After exposing you to the foundations of the machine and deep learning, you will use Python to build a bot and then teach it the game's rules. We also focus on how different technologies have revolutionized gaming and animation with various illustrations"--
Book Synopsis Probabilistic Deep Learning by : Oliver Duerr
Download or read book Probabilistic Deep Learning written by Oliver Duerr and published by Manning Publications. This book was released on 2020-11-10 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. About the book Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
Book Synopsis Simulating Humans by : Norman I. Badler
Download or read book Simulating Humans written by Norman I. Badler and published by Oxford University Press, USA. This book was released on 1993-09-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: The area of simulated human figures is an active research area in computer graphics, and Norman Badler's group at the University of Pennsylvania is one of the leaders in the field. This book summarizes the state of the art in simulating human figures, discusses many of the interesting application areas, and makes some assumptions and predictions about where the field is going.
Book Synopsis Hybrid Intelligence by : Philip F. Yuan
Download or read book Hybrid Intelligence written by Philip F. Yuan and published by Springer Nature. This book was released on 2023-04-03 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is a compilation of selected papers from DigitalFUTURES 2022—The 4th International Conference on Computational Design and Robotic Fabrication (CDRF 2022). The work focuses on novel techniques for computational design and robotic fabrication. The contents make valuable contributions to academic researchers, designers, and engineers in the industry. As well, readers encounter new ideas about intelligence in architecture.
