Download or read book Data driven Modelling and Scientific Machine Learning in Continuum Physics written by Krishna Garikipati and published by Springer Nature. This book was released on with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Tensor Voting written by Philippos Mordohai and published by Springer Nature. This book was released on 2022-06-01 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources.
Download or read book Nonequilibrium Statistical Mechanics written by Robert Zwanzig and published by Oxford University Press, USA. This book was released on 2001-05-17 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a presentation of the main ideas and methods of modern nonequilibrium statistical mechanics. It is the perfect introduction for anyone in chemistry or physics who needs an update or background in this time-dependent field. Topics covered include fluctuation-dissipation theorem; linear response theory; time correlation functions, and projection operators. Theoretical models are illustrated by real-world examples and numerous applications such as chemical reaction rates and spectral line shapes are covered. The mathematical treatments are detailed and easily understandable and the appendices include useful mathematical methods like the Laplace transforms, Gaussian random variables and phenomenological transport equations.
Download or read book Mathematical Modelling written by Seppo Pohjolainen and published by Springer. This book was released on 2016-07-14 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough introduction to the challenge of applying mathematics in real-world scenarios. Modelling tasks rarely involve well-defined categories, and they often require multidisciplinary input from mathematics, physics, computer sciences, or engineering. In keeping with this spirit of modelling, the book includes a wealth of cross-references between the chapters and frequently points to the real-world context. The book combines classical approaches to modelling with novel areas such as soft computing methods, inverse problems, and model uncertainty. Attention is also paid to the interaction between models, data and the use of mathematical software. The reader will find a broad selection of theoretical tools for practicing industrial mathematics, including the analysis of continuum models, probabilistic and discrete phenomena, and asymptotic and sensitivity analysis.
Download or read book Machine Learning in Heliophysics written by Thomas Berger and published by Frontiers Media SA. This book was released on 2021-11-24 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning in Geomechanics 2 written by Ioannis Stefanou and published by John Wiley & Sons. This book was released on 2024-10-11 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them. Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.
Download or read book Large Scale PDE Constrained Optimization written by Lorenz T. Biegler and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.
Download or read book Mathematics Applied to Continuum Mechanics written by Lee A. Segel and published by SIAM. This book was released on 2007-07-12 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic work gives an excellent overview of the subject, with an emphasis on clarity, explanation, and motivation. Extensive exercises and a valuable section containing hints and answers make this an excellent text for both classroom use and independent study.
Download or read book Numerical Analysis meets Machine Learning written by and published by Elsevier. This book was released on 2024-06-13 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical Analysis Meets Machine Learning series, highlights new advances in the field, with this new volume presenting interesting chapters. Each chapter is written by an international board of authors. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Numerical Analysis series - Updated release includes the latest information on the Numerical Analysis Meets Machine Learning
Download or read book Effective Lagrangians for the Standard Model written by Antonio Dobado and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to some recently developed techniques in quantum field theory (QFT), as well as to their main applications to different areas of parti cle physics. All together they are known as the effective or phenomenological Lagrangian formalism. Motivated by the enormous amount of work carried out in this field during the last years, our purpose when writing this book has been to give a modern and pedagogical exposition of the most relevant as pects of the topic. We hope that our efforts will be useful, both for graduated students in the search for a solid theoretical background in modern phe nomenology and for more experimented particle physicists willing to learn about this field or to start working on it. Even though we have tried to keep the book as self-contained as possible, it has been written assuming that the reader is familiar, at least, with the most basic concepts and techniques of QFT, gauge theories, the standard model (SM) and differential geometry, at the level of graduate studies. It is therefore possible that senior high-energy physicists may find the book too detailed and so they could probably omit several sections. The book is divided into two main parts and the appendices. In the first part we introduce the fundamentals of the effective Lagrangian formalism and other basic topics such as Ward identities, non-linear sigma models (NLSM), spontaneous symmetry breaking (SSB), anomalies, the SM symmetries, etc.
Download or read book Handbook of Materials Modeling written by Sidney Yip and published by Springer Science & Business Media. This book was released on 2007-11-17 with total page 2903 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first reference of its kind in the rapidly emerging field of computational approachs to materials research, this is a compendium of perspective-providing and topical articles written to inform students and non-specialists of the current status and capabilities of modelling and simulation. From the standpoint of methodology, the development follows a multiscale approach with emphasis on electronic-structure, atomistic, and mesoscale methods, as well as mathematical analysis and rate processes. Basic models are treated across traditional disciplines, not only in the discussion of methods but also in chapters on crystal defects, microstructure, fluids, polymers and soft matter. Written by authors who are actively participating in the current development, this collection of 150 articles has the breadth and depth to be a major contributor toward defining the field of computational materials. In addition, there are 40 commentaries by highly respected researchers, presenting various views that should interest the future generations of the community. Subject Editors: Martin Bazant, MIT; Bruce Boghosian, Tufts University; Richard Catlow, Royal Institution; Long-Qing Chen, Pennsylvania State University; William Curtin, Brown University; Tomas Diaz de la Rubia, Lawrence Livermore National Laboratory; Nicolas Hadjiconstantinou, MIT; Mark F. Horstemeyer, Mississippi State University; Efthimios Kaxiras, Harvard University; L. Mahadevan, Harvard University; Dimitrios Maroudas, University of Massachusetts; Nicola Marzari, MIT; Horia Metiu, University of California Santa Barbara; Gregory C. Rutledge, MIT; David J. Srolovitz, Princeton University; Bernhardt L. Trout, MIT; Dieter Wolf, Argonne National Laboratory.
Download or read book Data Driven Modeling Scientific Computation written by Jose Nathan Kutz and published by . This book was released on 2013-08-08 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.
Download or read book Geometric Design of Linkages written by J. Michael McCarthy and published by Springer Science & Business Media. This book was released on 2010-11-11 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to the mathematical theory of design for articulated mechanical systems known as linkages. The focus is on sizing mechanical constraints that guide the movement of a work piece, or end-effector, of the system. The function of the device is prescribed as a set of positions to be reachable by the end-effector; and the mechanical constraints are formed by joints that limit relative movement. The goal is to find all the devices that can achieve a specific task. Formulated in this way the design problem is purely geometric in character. Robot manipulators, walking machines, and mechanical hands are examples of articulated mechanical systems that rely on simple mechanical constraints to provide a complex workspace for the end- effector. The principles presented in this book form the foundation for a design theory for these devices. The emphasis, however, is on articulated systems with fewer degrees of freedom than that of the typical robotic system, and therefore, less complexity. This book will be useful to mathematics, engineering and computer science departments teaching courses on mathematical modeling of robotics and other articulated mechanical systems. This new edition includes research results of the past decade on the synthesis of multi loop planar and spherical linkages, and the use of homotopy methods and Clifford algebras in the synthesis of spatial serial chains. One new chapter on the synthesis of spatial serial chains introduces numerical homotopy and the linear product decomposition of polynomial systems. The second new chapter introduces the Clifford algebra formulation of the kinematics equations of serial chain robots. Examples are use throughout to demonstrate the theory.
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 Data driven Modeling for Diabetes written by Vasilis Marmarelis and published by Springer Science & Business. This book was released on 2014-04-22 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume presents computational models of diabetes that quantify the dynamic interrelationships among key physiological variables implicated in the underlying physiology under a variety of metabolic and behavioral conditions. These variables comprise for example blood glucose concentration and various hormones such as insulin, glucagon, epinephrine, norepinephrine as well as cortisol. The presented models provide a powerful diagnostic tool but may also enable treatment via long-term glucose regulation in diabetics through closed-look model-reference control using frequent insulin infusions, which are administered by implanted programmable micro-pumps. This research volume aims at presenting state-of-the-art research on this subject and demonstrating the potential applications of modeling to the diagnosis and treatment of diabetes. The target audience primarily comprises research and experts in the field but the book may also be beneficial for graduate students.
Download or read book Particulate Discrete Element Modelling written by Catherine O'Sullivan and published by CRC Press. This book was released on 2011-04-06 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first single work on DEM providing the information to get started with this powerful numerical modelling approach. Provides the basic details of the numerical method and the approaches used to interpret the results of DEM simulations. It will be of use to professionals, researchers and higher level students, with a theoretical overview of DEM as well as practical guidance.Selected Contents: 1.Introduction 2.Use of DEM in Geomechanics 3.Calculation of Contact Forces 4.Particle Motion 5.Particle Types 6.Boundary Conditions 7.Initial Geometry and Specimen Generation 8.Time Integration and Discrete Element Modelling 9.DEM Interpretation: A Continuum Perspective 10.Postprocessing: Graphical Interpretation of DEM Simulations 11.Basic Statisti
Download or read book Sensing Modeling and Optimization of Cardiac Systems written by Hui Yang and published by Springer Nature. This book was released on 2023-09-19 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients’ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.
