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| differentiable physics simulation of dynamics-augmented neural objects: Cloth Simulation for Computer Graphics Tuur Stuyck, 2018-08-24 Physics-based animation is commonplace in animated feature films and even special effects for live-action movies. Think about a recent movie and there will be some sort of special effects such as explosions or virtual worlds. Cloth simulation is no different and is ubiquitous because most virtual characters (hopefully!) wear some sort of clothing. The focus of this book is physics-based cloth simulation. We start by providing background information and discuss a range of applications. This book provides explanations of multiple cloth simulation techniques. More specifically, we start with the most simple explicitly integrated mass-spring model and gradually work our way up to more complex and commonly used implicitly integrated continuum techniques in state-of-the-art implementations. We give an intuitive explanation of the techniques and give additional information on how to efficiently implement them on a computer. This book discusses explicit and implicit integration schemes for cloth simulation modeled with mass-spring systems. In addition to this simple model, we explain the more advanced continuum-inspired cloth model introduced in the seminal work of Baraff and Witkin [1998]. This method is commonly used in industry. We also explain recent work by Liu et al. [2013] that provides a technique to obtain fast simulations. In addition to these simulation approaches, we discuss how cloth simulations can be art directed for stylized animations based on the work of Wojtan et al. [2006]. Controllability is an essential component of a feature animation film production pipeline. We conclude by pointing the reader to more advanced techniques. |
| differentiable physics simulation of dynamics-augmented neural objects: Pattern Recognition and Computer Vision Shiqi Yu, Zhaoxiang Zhang, Pong C. Yuen, Junwei Han, Tieniu Tan, Yike Guo, Jianhuang Lai, Jianguo Zhang, 2022-10-27 The 4-volume set LNCS 13534, 13535, 13536 and 13537 constitutes the refereed proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022, held in Shenzhen, China, in November 2022. The 233 full papers presented were carefully reviewed and selected from 564 submissions. The papers have been organized in the following topical sections: Theories and Feature Extraction; Machine learning, Multimedia and Multimodal; Optimization and Neural Network and Deep Learning; Biomedical Image Processing and Analysis; Pattern Classification and Clustering; 3D Computer Vision and Reconstruction, Robots and Autonomous Driving; Recognition, Remote Sensing; Vision Analysis and Understanding; Image Processing and Low-level Vision; Object Detection, Segmentation and Tracking. |
| differentiable physics simulation of dynamics-augmented neural objects: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.” |
| differentiable physics simulation of dynamics-augmented neural objects: Simulating Humans Norman I. Badler, Cary B. Phillips, Bonnie Lynn Webber, 1993-06-17 During the past decade, high-performance computer graphics have found application in an exciting and expanding range of new domains. Among the most dramatic developments has been the incorporation of real-time interactive manipulation and display for human figures. Though actively pursued by several research groups, the problem of providing a synthetic or surrogate human for engineers and designers already familiar with computer-aided design techniques was most comprehensively solved by Norman Badler's computer graphics laboratory at the University of Pennsylvania. The breadth of that effort as well as the details of its methodology and software environment are presented in this volume. The book is intended for human factors engineers interested in understanding how a computer-graphics surrogate human can augment their analyses of designed environments. It will also inform design engineers of the state of the art in human figure modeling, and hence of the human-centered design central to the emergent concept of concurrent engineering. In fulfilling these goals, the book additionally documents for the entire computer graphics community a major research effort in the interactive control of articulated human figures. |
| differentiable physics simulation of dynamics-augmented neural objects: The Nature of Explanation K. J. W. Craik, 1967-10 In his only complete work of any length, Kenneth Craik considers thought as a term for the conscious working of a highly complex machine. |
| differentiable physics simulation of dynamics-augmented neural objects: Applied Stochastic Differential Equations Simo Särkkä, Arno Solin, 2019-05-02 With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice. |
| differentiable physics simulation of dynamics-augmented neural objects: Hybrid Neural Systems Stefan Wermter, Ron Sun, 2000-03-29 Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems. The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches. |
| differentiable physics simulation of dynamics-augmented neural objects: A Mathematical Introduction to Robotic Manipulation Richard M. Murray, 2017-12-14 A Mathematical Introduction to Robotic Manipulation presents a mathematical formulation of the kinematics, dynamics, and control of robot manipulators. It uses an elegant set of mathematical tools that emphasizes the geometry of robot motion and allows a large class of robotic manipulation problems to be analyzed within a unified framework. The foundation of the book is a derivation of robot kinematics using the product of the exponentials formula. The authors explore the kinematics of open-chain manipulators and multifingered robot hands, present an analysis of the dynamics and control of robot systems, discuss the specification and control of internal forces and internal motions, and address the implications of the nonholonomic nature of rolling contact are addressed, as well. The wealth of information, numerous examples, and exercises make A Mathematical Introduction to Robotic Manipulation valuable as both a reference for robotics researchers and a text for students in advanced robotics courses. |
| differentiable physics simulation of dynamics-augmented neural objects: Self-Organization and Associative Memory Teuvo Kohonen, 2012-12-06 Two significant things have happened since the writing of the first edition in 1983. One of them is recent arousal of strong interest in general aspects of neural computing, or neural networks, as the previous neural models are nowadays called. The incentive, of course, has been to develop new com puters. Especially it may have been felt that the so-called fifth-generation computers, based on conventional logic programming, do not yet contain in formation processing principles of the same type as those encountered in the brain. All new ideas for the neural computers are, of course, welcome. On the other hand, it is not very easy to see what kind of restrictions there exist to their implementation. In order to approach this problem systematically, cer tain lines of thought, disciplines, and criteria should be followed. It is the pur pose of the added Chapter 9 to reflect upon such problems from a general point of view. Another important thing is a boom of new hardware technologies for dis tributed associative memories, especially high-density semiconductor circuits, and optical materials and components. The era is very close when the parallel processors can be made all-optical. Several working associative memory archi tectures, based solely on optical technologies, have been constructed in recent years. For this reason it was felt necessary to include a separate chapter (Chap. 10) which deals with the optical associative memories. Part of its con tents is taken over from the first edition. |
| differentiable physics simulation of dynamics-augmented neural objects: The Playful Machine Ralf Der, Georg Martius, 2012-01-11 Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complex machines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? The book provides answers by developing a general principle---homeokinesis, the dynamical symbiosis between brain, body, and environment---that is shown to drive robots to self- determined, individual development in a playful and obviously embodiment- related way: a dog-like robot starts playing with a barrier, eventually jumping or climbing over it; a snakebot develops coiling and jumping modes; humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarios when encountering an opponent. The book also develops guided self-organization, a new method that helps to make the playful machines fit for fulfilling tasks in the real world. The book provides two levels of presentation. Students and scientific researchers interested in the field of robotics, self-organization and dynamical systems theory may be satisfied by the in-depth mathematical analysis of the principle, the bootstrapping scenarios, and the emerging behaviors. But the book additionally comes with a robotics simulator inviting also the non- scientific reader to simply enjoy the fabulous world of playful machines by performing the numerous experiments. |
| differentiable physics simulation of dynamics-augmented neural objects: Machine Learning Meets Quantum Physics Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller, 2020-06-03 Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context. |
| differentiable physics simulation of dynamics-augmented neural objects: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller, 2019-09-10 The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI. |
| differentiable physics simulation of dynamics-augmented neural objects: The Principles of Deep Learning Theory Daniel A. Roberts, Sho Yaida, Boris Hanin, 2022-05-26 This volume develops an effective theory approach to understanding deep neural networks of practical relevance. |
| differentiable physics simulation of dynamics-augmented neural objects: Rigid Body Dynamics Algorithms Roy Featherstone, 2014-11-10 Rigid Body Dynamics Algorithms presents the subject of computational rigid-body dynamics through the medium of spatial 6D vector notation. It explains how to model a rigid-body system and how to analyze it, and it presents the most comprehensive collection of the best rigid-body dynamics algorithms to be found in a single source. The use of spatial vector notation greatly reduces the volume of algebra which allows systems to be described using fewer equations and fewer quantities. It also allows problems to be solved in fewer steps, and solutions to be expressed more succinctly. In addition algorithms are explained simply and clearly, and are expressed in a compact form. The use of spatial vector notation facilitates the implementation of dynamics algorithms on a computer: shorter, simpler code that is easier to write, understand and debug, with no loss of efficiency. |
| differentiable physics simulation of dynamics-augmented neural objects: Dynamics Of Complex Systems Yaneer Bar-yam, 2019-03-04 This book aims to develop models and modeling techniques that are useful when applied to all complex systems. It adopts both analytic tools and computer simulation. The book is intended for students and researchers with a variety of backgrounds. |
| differentiable physics simulation of dynamics-augmented neural objects: Coarse-Graining of Condensed Phase and Biomolecular Systems Gregory A. Voth, 2008-09-22 Exploring recent developments in the field, Coarse-Graining of Condensed Phase and Biomolecular Systems examines systematic ways of constructing coarse-grained representations for complex systems. It explains how this approach can be used in the simulation and modeling of condensed phase and biomolecular systems. Assembling some of the most influential, world-renowned researchers in the field, this book covers the latest developments in the coarse-grained molecular dynamics simulation and modeling of condensed phase and biomolecular systems. Each chapter focuses on specific examples of evolving coarse-graining methodologies and presents results for a variety of complex systems. The contributors discuss the minimalist, inversion, and multiscale approaches to coarse-graining, along with the emerging challenges of coarse-graining. They also connect atomic-level information with new coarse-grained representations of complex systems, such as lipid bilayers, proteins, peptides, and DNA. |
| differentiable physics simulation of dynamics-augmented neural objects: Current Research in Sports Sciences R. Maughan, V.A. Rogozkin, 1996-10-31 There are two main reasons for pursuing research in the Sports Sciences. Firstly, by studying responses to exercise, we learn about the normal function of the tissues and or gans whose function allows exercise to be performed. The genetic endowment of elite ath letes is a major factor in their success, and they represent one end of the continuum of human performance capability: the study of elite athletes also demonstrates the limits of human adaptation because nowhere else is the body subjected to such levels of intensive exercise on a regular basis. The second reason for studying Sports Science is the intrinsic interest and value of the subject itself. Elite performers set levels to which others can as pire, but even among spectators, sport is an important part oflife and society. of top sport and elite performers, there is also another reason Apart from the study for medical and scientific interest in sport. There is no longer any doubt that lack ofphysi cal activity is a major risk factor for many of the diseases that affect people in all coun tries: such diseases include coronary heart disease, obesity, hypertension, and diabetes. An increased level of recreational physical activity is now an accepted part of the prescription for treatment and prevention of many illnesses, including those with psychological as well as physical causes. An understanding of the normal response to exercise, as well as of the role of exercise in disease prevention, is therefore vital. |
| differentiable physics simulation of dynamics-augmented neural objects: Adaptive Control of Nonsmooth Dynamic Systems Gang Tao, Frank L. Lewis, 2001-09-26 Many of the non-smooth, non-linear phenomena covered in this well-balanced book are of vital importance in almost any field of engineering. Contributors from all over the world ensure that no one area’s slant on the subjects predominates. |
| differentiable physics simulation of dynamics-augmented neural objects: Free Energy Computations Tony Lelivre, Gabriel Stoltz, Mathias Rousset, 2010 This monograph provides a general introduction to advanced computational methods for free energy calculations, from the systematic and rigorous point of view of applied mathematics. Free energy calculations in molecular dynamics have become an outstanding and increasingly broad computational field in physics, chemistry and molecular biology within the past few years, by making possible the analysis of complex molecular systems. This work proposes a new, general and rigorous presentation, intended both for practitioners interested in a mathematical treatment, and for applied mathematicians interested in molecular dynamics. |
| differentiable physics simulation of dynamics-augmented neural objects: Building Aerodynamics Tom Lawson, 2001-04-16 This book starts, by explaining briefly the origins of wind. It then proceeds to the normal forms of presentation for wind data, and explains how each is used in the appropriate analysis. The general aerodynamics of bluff bodies is explained in Chapter 2.Wind loading, wind environment, rain, ventilation, fire and effluent from chimneys are considered in the following chapters. Experimental methods are discussed in the penultimate chapter. Up to this point, theory and practice are discussed, and no design data are presented.Necessary statistics insofar as they concern the earlier chapter material are presented in the last chapter. This is not a theoretical study, but simply pointing the reader to the appropriate statistical technique and presents the relevant expressions. |
| differentiable physics simulation of dynamics-augmented neural objects: Identification of Dynamic Systems Rolf Isermann, Marco Münchhof, 2011-04-08 Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing. |
| differentiable physics simulation of dynamics-augmented neural objects: Pragmatic Introduction To The Finite Element Method For Thermal And Stress Analysis, A: With The Matlab Toolkit Sofea Petr Krysl, 2006-10-23 This textbook provides an accessible and self-contained description of the Galerkin finite element method for the two important models of continuum mechanics, transient heat conduction and elastodynamics, from formulation of the governing equations to implementation in Matlab.The coverage follows an intuitive approach: the salient features of each initial boundary value problem are reviewed, including a thorough description of the boundary conditions; the method of weighted residuals is applied to derive the discrete equations; and clear examples are introduced to illustrate the method. |
| differentiable physics simulation of dynamics-augmented neural objects: Knowledge-Based Simulation Paul A. Fishwick, Richard B. Modjeski, 2012-12-06 Knowledge-Based Simulation: Methodology and Application represents a recent compilation of research material that reviews fundamental concepts of simulation methodology and knowledge-based simulation applications. Knowledge-based simulation represents a new and exciting bridge area linking the fields of computer simulation and artificial intelligence. This book will appeal to both theorists and practitioners who require simulation to solve complex problems. A primary attraction of the book is its emphasis on both methodology and applications. In this way, the reader can explore new methods for encoding knowledge-inten- sive information into a simulation model, and new applications that utilize these methods. |
| differentiable physics simulation of dynamics-augmented neural objects: NANO-CHIPS 2030 Boris Murmann, Bernd Hoefflinger, 2020-06-08 In this book, a global team of experts from academia, research institutes and industry presents their vision on how new nano-chip architectures will enable the performance and energy efficiency needed for AI-driven advancements in autonomous mobility, healthcare, and man-machine cooperation. Recent reviews of the status quo, as presented in CHIPS 2020 (Springer), have prompted the need for an urgent reassessment of opportunities in nanoelectronic information technology. As such, this book explores the foundations of a new era in nanoelectronics that will drive progress in intelligent chip systems for energy-efficient information technology, on-chip deep learning for data analytics, and quantum computing. Given its scope, this book provides a timely compendium that hopes to inspire and shape the future of nanoelectronics in the decades to come. |
| differentiable physics simulation of dynamics-augmented neural objects: General Video Game Artificial Intelligence Diego Pérez Liébana, Simon M. Lucas, Raluca D. Gaina, Julian Togelius, Ahmed Khalifa, Jialin Liu, 2019-10-09 Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge. The general video game AI framework and competition propose a challenge in which researchers can test their favorite AI methods with a potentially infinite number of games created using the Video Game Description Language. The open-source framework has been used since 2014 for running a challenge. Competitors around the globe submit their best approaches that aim to generalize well across games. Additionally, the framework has been used in AI modules by many higher-education institutions as assignments, or as proposed projects for final year (undergraduate and Master's) students and Ph.D. candidates. The present book, written by the developers and organizers of the framework, presents the most interesting highlights of the research performed by the authors during these years in this domain. It showcases work on methods to play the games, generators of content, and video game optimization. It also outlines potential further work in an area that offers multiple research directions for the future. |
| differentiable physics simulation of dynamics-augmented neural objects: 2021 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Staff, 2021-06-20 CVPR is the premier annual computer vision event comprising the main conference and several co located workshops and short courses With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers |
| differentiable physics simulation of dynamics-augmented neural objects: Pattern Recognition and Machine Learning Christopher M. Bishop, 2016-08-23 This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. |
| differentiable physics simulation of dynamics-augmented neural objects: Neuromorphic Photonics Paul R. Prucnal, Bhavin J. Shastri, 2017-05-08 This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of neuromorphic photonics. It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field. |
| differentiable physics simulation of dynamics-augmented neural objects: Direct Methods for Sparse Linear Systems Timothy A. Davis, 2006-09-01 The sparse backslash book. Everything you wanted to know but never dared to ask about modern direct linear solvers. Chen Greif, Assistant Professor, Department of Computer Science, University of British Columbia.Overall, the book is magnificent. It fills a long-felt need for an accessible textbook on modern sparse direct methods. Its choice of scope is excellent John Gilbert, Professor, Department of Computer Science, University of California, Santa Barbara.Computational scientists often encounter problems requiring the solution of sparse systems of linear equations. Attacking these problems efficiently requires an in-depth knowledge of the underlying theory, algorithms, and data structures found in sparse matrix software libraries. Here, Davis presents the fundamentals of sparse matrix algorithms to provide the requisite background. The book includes CSparse, a concise downloadable sparse matrix package that illustrates the algorithms and theorems presented in the book and equips readers with the tools necessary to understand larger and more complex software packages.With a strong emphasis on MATLAB and the C programming language, Direct Methods for Sparse Linear Systems equips readers with the working knowledge required to use sparse solver packages and write code to interface applications to those packages. The book also explains how MATLAB performs its sparse matrix computations.Audience This invaluable book is essential to computational scientists and software developers who want to understand the theory and algorithms behind modern techniques used to solve large sparse linear systems. The book also serves as an excellent practical resource for students with an interest in combinatorial scientific computing.Preface; Chapter 1: Introduction; Chapter 2: Basic algorithms; Chapter 3: Solving triangular systems; Chapter 4: Cholesky factorization; Chapter 5: Orthogonal methods; Chapter 6: LU factorization; Chapter 7: Fill-reducing orderings; Chapter 8: Solving sparse linear systems; Chapter 9: CSparse; Chapter 10: Sparse matrices in MATLAB; Appendix: Basics of the C programming language; Bibliography; Index. |
| differentiable physics simulation of dynamics-augmented neural objects: Program Synthesis Sumit Gulwani, Oleksandr Polozov, Rishabh Singh, 2017-07-11 Program synthesis is the task of automatically finding a program in the underlying programming language that satisfies the user intent expressed in the form of some specification. Since the inception of artificial intelligence in the 1950s, this problem has been considered the holy grail of Computer Science. Despite inherent challenges in the problem such as ambiguity of user intent and a typically enormous search space of programs, the field of program synthesis has developed many different techniques that enable program synthesis in different real-life application domains. It is now used successfully in software engineering, biological discovery, compute-raided education, end-user programming, and data cleaning. In the last decade, several applications of synthesis in the field of programming by examples have been deployed in mass-market industrial products. This monograph is a general overview of the state-of-the-art approaches to program synthesis, its applications, and subfields. It discusses the general principles common to all modern synthesis approaches such as syntactic bias, oracle-guided inductive search, and optimization techniques. We then present a literature review covering the four most common state-of-the-art techniques in program synthesis: enumerative search, constraint solving, stochastic search, and deduction-based programming by examples. It concludes with a brief list of future horizons for the field. |
| differentiable physics simulation of dynamics-augmented neural objects: Multithreading for Visual Effects Martin Watt, Erwin Coumans, George ElKoura, Ronald Henderson, Manuel Kraemer, Jeff Lait, James Reinders, 2014-07-29 Tackle the Challenges of Parallel Programming in the Visual Effects IndustryIn Multithreading for Visual Effects, developers from DreamWorks Animation, Pixar, Side Effects, Intel, and AMD share their successes and failures in the messy real-world application area of production software. They provide practical advice on multithreading techniques and |
| differentiable physics simulation of dynamics-augmented neural objects: Bayesian Reinforcement Learning Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar, 2015-11-18 Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties. |
| differentiable physics simulation of dynamics-augmented neural objects: Reinforcement Learning, second edition Richard S. Sutton, Andrew G. Barto, 2018-11-13 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. |
| differentiable physics simulation of dynamics-augmented neural objects: Graph Representation Learning William L. William L. Hamilton, 2022-06-01 Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning. |
| differentiable physics simulation of dynamics-augmented neural objects: Identification for Automotive Systems Daniel Alberer, Håkan Hjalmarsson, Luigi del Re, 2011-12-09 Increasing complexity and performance and reliability expectations make modeling of automotive system both more difficult and more urgent. Automotive control has slowly evolved from an add-on to classical engine and vehicle design to a key technology to enforce consumption, pollution and safety limits. Modeling, however, is still mainly based on classical methods, even though much progress has been done in the identification community to speed it up and improve it. This book, the product of a workshop of representatives of different communities, offers an insight on how to close the gap and exploit this progress for the next generations of vehicles. |
| differentiable physics simulation of dynamics-augmented neural objects: Photons and Atoms Claude Cohen-Tannoudji, Jacques Dupont-Roc, Gilbert Grynberg, 1989-08-04 Photons and Atoms Photons and Atoms: Introduction to Quantum Electrodynamics provides the necessary background to understand the various physical processes associated with photon-atom interactions. It starts with elementary quantum theory and classical electrodynamics and progresses to more advanced approaches. A critical comparison is made between these different, although equivalent, formulations of quantum electrodynamics. Using this format, the reader is offered a gradual, yet flexible introduction to quantum electrodynamics, avoiding formal discussions and excessive shortcuts. Complementing each chapter are numerous examples and exercises that can be used independently from the rest of the book to extend each chapter in many disciplines depending on the interests and needs of the reader. |
| differentiable physics simulation of dynamics-augmented neural objects: Group and Crowd Behavior for Computer Vision Vittorio Murino, Marco Cristani, Shishir Shah, Silvio Savarese, 2017-04-18 Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. - Presents the first book to cover the topic of modeling and analysis of groups in computer vision - Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms - Focuses on group and crowd analysis metrics - Discusses real industrial systems dealing with the problem of analyzing groups and crowds |
| differentiable physics simulation of dynamics-augmented neural objects: Theoretical Neuroscience Peter Dayan, Laurence F. Abbott, 2005-08-12 Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site. |
| differentiable physics simulation of dynamics-augmented neural objects: Empirical Inference Bernhard Schölkopf, Zhiyuan Luo, Vladimir Vovk, 2013-12-11 This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection. These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning. |
Differentiable Physics Simulation of Dynamics-Augmented …
W E PRESENT the Dynamics-Augmented Neural Object (DANO), a novel object representation that augments a neural object with dynamical properties, so that its motion under applied …
Differentiable Physics Simulation of Dynamics-Augmented …
We present the Dynamics-Augmented Neural Object (DANO), a novel object representation that augments a neural object with dynamical properties, so that its motion under
DIFFERENTIABLE PHYSICS SIMULATION - OpenReview
Differentiable physics simulation is a powerful family of new techniques that applies gradient-based methods to learning and control of physical systems. It enables optimization for control, …
Differentiable Physics Simulation Of Dynamics Augmented …
provides explanations of multiple cloth simulation techniques More specifically we start with the most simple explicitly integrated mass spring model and gradually work our way up to more …
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differentiable physics simulation of dynamics-augmented neural objects: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 This book provides a comprehensive and self …
Augmenting Differentiable Simulators with Neural Networks …
We propose a technique for hybrid simulation that leverages differentiable physics models and neural networks to allow for efficient system identification, design optimization, and gradient …
Differentiable Physics Simulation Of Dynamics Augmented …
physics based cloth simulation We start by providing background information and discuss a range of applications This book provides explanations of multiple cloth simulation techniques More …
NeuralSim: Augmenting Differentiable Simulators with Neural …
In this work, we study the augmentation of a novel differentiable rigid-body physics engine via neural networks that is able to learn nonlinear relationships between dynamic quantities and …
Differentiable Physics Simulation Of Dynamics Augmented …
Table of Contents Differentiable Physics Simulation Of Dynamics Augmented Neural Objects 1. Understanding the eBook Differentiable Physics Simulation Of Dynamics Augmented Neural …
NeuralSim: Augmenting Differentiable Simulators with Neural …
In this work, we study the augmentation of a novel differentiable rigid-body physics engine via neural networks that is able to learn nonlinear relationships between dynamic quantities and …
arXiv:2207.05060v1 [cs.LG] 8 Jul 2022
contact models for differentiability. In this paper, we overview four kinds of differen-tiable contact formulations - linear complemen-tarity problems (LCP), convex optimization mod-els, …
Differentiable Physics Simulation Of Dynamics Augmented …
Differentiable Physics Simulation Of Dynamics Augmented Neural Objects: The ChainQueen Differentiable Physics Engine Yuanming Hu (S. M.),2019 Physical simulators have been widely …
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Differentiable Physics Simulation Of Dynamics Augmented Neural Objects: In the digital age, access to information has become easier than ever before. The ability to download …
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Differentiable Physics Simulation Of Dynamics Augmented Neural Objects: In the digital age, access to information has become easier than ever before. The ability to download …
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deep neural networks of practical relevance Dynamics Of Complex Systems Yaneer Bar-yam,2019-03-04 This book aims to develop models and modeling techniques that are useful …
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Differentiable Physics Simulation of Dynamics …
W E PRESENT the Dynamics-Augmented Neural Object (DANO), a novel object representation that augments a neural object with dynamical properties, so that its motion under applied …
Differentiable Physics Simulation of Dynamics …
We present the Dynamics-Augmented Neural Object (DANO), a novel object representation that augments a neural object with dynamical properties, so that its motion under
DIFFERENTIABLE PHYSICS SIMULATION - OpenReview
Differentiable physics simulation is a powerful family of new techniques that applies gradient-based methods to learning and control of physical systems. It enables optimization for control, …
Differentiable Physics Simulation Of Dynamics …
provides explanations of multiple cloth simulation techniques More specifically we start with the most simple explicitly integrated mass spring model and gradually work our way up to more …
Differentiable Physics Simulation Of Dynamics …
differentiable physics simulation of dynamics-augmented neural objects: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 This book provides a comprehensive and self …
Augmenting Differentiable Simulators with Neural …
We propose a technique for hybrid simulation that leverages differentiable physics models and neural networks to allow for efficient system identification, design optimization, and gradient …
Differentiable Physics Simulation Of Dynamics …
physics based cloth simulation We start by providing background information and discuss a range of applications This book provides explanations of multiple cloth simulation techniques More …
NeuralSim: Augmenting Differentiable Simulators with …
In this work, we study the augmentation of a novel differentiable rigid-body physics engine via neural networks that is able to learn nonlinear relationships between dynamic quantities and …
Differentiable Physics Simulation Of Dynamics …
Table of Contents Differentiable Physics Simulation Of Dynamics Augmented Neural Objects 1. Understanding the eBook Differentiable Physics Simulation Of Dynamics Augmented Neural …
NeuralSim: Augmenting Differentiable Simulators with …
In this work, we study the augmentation of a novel differentiable rigid-body physics engine via neural networks that is able to learn nonlinear relationships between dynamic quantities and …
arXiv:2207.05060v1 [cs.LG] 8 Jul 2022
contact models for differentiability. In this paper, we overview four kinds of differen-tiable contact formulations - linear complemen-tarity problems (LCP), convex optimization mod-els, …
Differentiable Physics Simulation Of Dynamics …
Differentiable Physics Simulation Of Dynamics Augmented Neural Objects: The ChainQueen Differentiable Physics Engine Yuanming Hu (S. M.),2019 Physical simulators have been …
Differentiable Physics Simulation Of Dynamics …
Differentiable Physics Simulation Of Dynamics Augmented Neural Objects: In the digital age, access to information has become easier than ever before. The ability to download …
Differentiable Physics Simulation Of Dynamics …
Differentiable Physics Simulation Of Dynamics Augmented Neural Objects: In the digital age, access to information has become easier than ever before. The ability to download …
Differentiable Physics Simulation Of Dynamics …
deep neural networks of practical relevance Dynamics Of Complex Systems Yaneer Bar-yam,2019-03-04 This book aims to develop models and modeling techniques that are useful …
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Differentiable Physics Simulation Of Dynamics Augmented Neural Objects eBook Subscription Services Differentiable Physics Simulation Of Dynamics Augmented Neural Objects Budget …
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Differentiable Physics Simulation Of Dynamics Augmented Neural Objects Book Review: Unveiling the Power of Words In a global driven by information and connectivity, the power of …
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This article will explore the advantages of Differentiable Physics Simulation Of Dynamics Augmented Neural Objects books and manuals for download, along with some popular …
