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Acta Numerica 2019 Volume 28
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Book Synopsis Acta Numerica 2019: Volume 28 by : Arieh Iserles
Download or read book Acta Numerica 2019: Volume 28 written by Arieh Iserles and published by Cambridge University Press. This book was released on 2019-09-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Acta Numerica is an annual publication containing invited survey papers by leading researchers in numerical mathematics and scientific computing. The papers present overviews of recent developments in their area and provide state-of-the-art techniques and analysis.
Book Synopsis Proceedings of International Conference on Computational Intelligence by : Ritu Tiwari
Download or read book Proceedings of International Conference on Computational Intelligence written by Ritu Tiwari and published by Springer Nature. This book was released on 2021-10-01 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents high quality research papers presented at International Conference on Computational Intelligence (ICCI 2020) held at Indian Institute of Information Technology, Pune, India during 12–13 December, 2020. The topics covered are artificial intelligence, neural network, deep learning techniques, fuzzy theory and systems, rough sets, self-organizing maps, machine learning, chaotic systems, multi-agent systems, computational optimization ensemble classifiers, reinforcement learning, decision trees, support vector machines, hybrid learning, statistical learning. metaheuristics algorithms: evolutionary and swarm-based algorithms like genetic algorithms, genetic programming, differential evolution, particle swarm optimization, whale optimization, spider monkey optimization, firefly algorithm, memetic algorithms. And also machine vision, Internet of Things, image processing, image segmentation, data clustering, sentiment analysis, big data, computer networks, signal processing, supply chain management, web and text mining, distributed systems, bioinformatics, embedded systems, expert system, forecasting, pattern recognition, planning and scheduling, time series analysis, human-computer interaction, web mining, natural language processing, multimedia systems, and quantum computing.
Book Synopsis Modeling And Simulation For Collective Dynamics by : Weizhu Bao
Download or read book Modeling And Simulation For Collective Dynamics written by Weizhu Bao and published by World Scientific. This book was released on 2023-01-17 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thematic program Quantum and Kinetic Problems: Modeling, Analysis, Numerics and Applications was held at the Institute for Mathematical Sciences at the National University of Singapore, from September 2019 to March 2020. Leading experts presented tutorials and special lectures geared towards the participating graduate students and junior researchers.Readers will find in this significant volume four expanded lecture notes with self-contained tutorials on modeling and simulation for collective dynamics including individual and population approaches for population dynamics in mathematical biology, collective behaviors for Lohe type aggregation models, mean-field particle swarm optimization, and consensus-based optimization and ensemble Kalman inversion for global optimization problems with constraints.This volume serves to inspire graduate students and researchers who will embark into original research work in kinetic models for collective dynamics and their applications.
Book Synopsis Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging by : Ke Chen
Download or read book Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging written by Ke Chen and published by Springer Nature. This book was released on 2023-02-24 with total page 1981 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook gathers together the state of the art on mathematical models and algorithms for imaging and vision. Its emphasis lies on rigorous mathematical methods, which represent the optimal solutions to a class of imaging and vision problems, and on effective algorithms, which are necessary for the methods to be translated to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enables the use of advanced tools from calculus, functions and calculus of variations, and nonlinear optimization, and provides the basis of high-resolution imaging through geometry and variational models. Besides, optimization naturally connects traditional model-driven approaches to the emerging data-driven approaches of machine and deep learning. No other framework can provide comparable accuracy and precision to imaging and vision. Written by leading researchers in imaging and vision, the chapters in this handbook all start with gentle introductions, which make this work accessible to graduate students. For newcomers to the field, the book provides a comprehensive and fast-track introduction to the content, to save time and get on with tackling new and emerging challenges. For researchers, exposure to the state of the art of research works leads to an overall view of the entire field so as to guide new research directions and avoid pitfalls in moving the field forward and looking into the next decades of imaging and information services. This work can greatly benefit graduate students, researchers, and practitioners in imaging and vision; applied mathematicians; medical imagers; engineers; and computer scientists.
Book Synopsis Stochastic Transport in Upper Ocean Dynamics II by : Bertrand Chapron
Download or read book Stochastic Transport in Upper Ocean Dynamics II written by Bertrand Chapron and published by Springer Nature. This book was released on 2023-11-04 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access proceedings volume brings selected, peer-reviewed contributions presented at the Third Stochastic Transport in Upper Ocean Dynamics (STUOD) 2022 Workshop, held virtually and in person at the Imperial College London, UK, September 26–29, 2022. The STUOD project is supported by an ERC Synergy Grant, and led by Imperial College London, the National Institute for Research in Computer Science and Automatic Control (INRIA) and the French Research Institute for Exploitation of the Sea (IFREMER). The project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. It will provide decision makers a means of quantifying the effects of local patterns of sea level rise, heat uptake, carbon storage and change of oxygen content and pH in the ocean. Its multimodal monitoring will enhance the scientific understanding of marine debris transport, tracking of oil spills and accumulation of plastic in the sea. All topics of these proceedings are essential to the scientific foundations of oceanography which has a vital role in climate science. Studies convened in this volume focus on a range of fundamental areas, including: Observations at a high resolution of upper ocean properties such as temperature, salinity, topography, wind, waves and velocity; Large scale numerical simulations; Data-based stochastic equations for upper ocean dynamics that quantify simulation error; Stochastic data assimilation to reduce uncertainty. These fundamental subjects in modern science and technology are urgently required in order to meet the challenges of climate change faced today by human society. This proceedings volume represents a lasting legacy of crucial scientific expertise to help meet this ongoing challenge, for the benefit of academics and professionals in pure and applied mathematics, computational science, data analysis, data assimilation and oceanography.
Book Synopsis The Sequential Quadratic Hamiltonian Method by : Alfio Borzì
Download or read book The Sequential Quadratic Hamiltonian Method written by Alfio Borzì and published by CRC Press. This book was released on 2023-05-26 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sequential quadratic hamiltonian (SQH) method is a novel numerical optimization procedure for solving optimal control problems governed by differential models. It is based on the characterisation of optimal controls in the framework of the Pontryagin maximum principle (PMP). The SQH method is a powerful computational methodology that is capable of development in many directions. The Sequential Quadratic Hamiltonian Method: Solving Optimal Control Problems discusses its analysis and use in solving nonsmooth ODE control problems, relaxed ODE control problems, stochastic control problems, mixed-integer control problems, PDE control problems, inverse PDE problems, differential Nash game problems, and problems related to residual neural networks. This book may serve as a textbook for undergraduate and graduate students, and as an introduction for researchers in sciences and engineering who intend to further develop the SQH method or wish to use it as a numerical tool for solving challenging optimal control problems and for investigating the Pontryagin maximum principle on new optimisation problems. Features Provides insight into mathematical and computational issues concerning optimal control problems, while discussing many differential models of interest in different disciplines. Suitable for undergraduate and graduate students and as an introduction for researchers in sciences and engineering. Accompanied by codes which allow the reader to apply the SQH method to solve many different optimal control and optimisation problems.
Book Synopsis Computational Science – ICCS 2020 by : Valeria V. Krzhizhanovskaya
Download or read book Computational Science – ICCS 2020 written by Valeria V. Krzhizhanovskaya and published by Springer Nature. This book was released on 2020-06-19 with total page 679 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.
Book Synopsis High-Dimensional Optimization and Probability by : Ashkan Nikeghbali
Download or read book High-Dimensional Optimization and Probability written by Ashkan Nikeghbali and published by Springer Nature. This book was released on 2022-08-04 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces. The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas. Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Book Synopsis Deterministic and Stochastic Optimal Control and Inverse Problems by : Baasansuren Jadamba
Download or read book Deterministic and Stochastic Optimal Control and Inverse Problems written by Baasansuren Jadamba and published by CRC Press. This book was released on 2021-12-15 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inverse problems of identifying parameters and initial/boundary conditions in deterministic and stochastic partial differential equations constitute a vibrant and emerging research area that has found numerous applications. A related problem of paramount importance is the optimal control problem for stochastic differential equations. This edited volume comprises invited contributions from world-renowned researchers in the subject of control and inverse problems. There are several contributions on optimal control and inverse problems covering different aspects of the theory, numerical methods, and applications. Besides a unified presentation of the most recent and relevant developments, this volume also presents some survey articles to make the material self-contained. To maintain the highest level of scientific quality, all manuscripts have been thoroughly reviewed.
Book Synopsis Evaluation Complexity of Algorithms for Nonconvex Optimization by : Coralia Cartis
Download or read book Evaluation Complexity of Algorithms for Nonconvex Optimization written by Coralia Cartis and published by SIAM. This book was released on 2022-07-06 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: A popular way to assess the “effort” needed to solve a problem is to count how many evaluations of the problem functions (and their derivatives) are required. In many cases, this is often the dominating computational cost. Given an optimization problem satisfying reasonable assumptions—and given access to problem-function values and derivatives of various degrees—how many evaluations might be required to approximately solve the problem? Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation, and Perspectives addresses this question for nonconvex optimization problems, those that may have local minimizers and appear most often in practice. This is the first book on complexity to cover topics such as composite and constrained optimization, derivative-free optimization, subproblem solution, and optimal (lower and sharpness) bounds for nonconvex problems. It is also the first to address the disadvantages of traditional optimality measures and propose useful surrogates leading to algorithms that compute approximate high-order critical points, and to compare traditional and new methods, highlighting the advantages of the latter from a complexity point of view. This is the go-to book for those interested in solving nonconvex optimization problems. It is suitable for advanced undergraduate and graduate students in courses on advanced numerical analysis, data science, numerical optimization, and approximation theory.
Book Synopsis Three-Dimensional Navier-Stokes Equations for Turbulence by : Luigi C. Berselli
Download or read book Three-Dimensional Navier-Stokes Equations for Turbulence written by Luigi C. Berselli and published by Academic Press. This book was released on 2021-03-10 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three-Dimensional Navier-Stokes Equations for Turbulence provides a rigorous but still accessible account of research into local and global energy dissipation, with particular emphasis on turbulence modeling. The mathematical detail is combined with coverage of physical terms such as energy balance and turbulence to make sure the reader is always in touch with the physical context. All important recent advancements in the analysis of the equations, such as rigorous bounds on structure functions and energy transfer rates in weak solutions, are addressed, and connections are made to numerical methods with many practical applications. The book is written to make this subject accessible to a range of readers, carefully tackling interdisciplinary topics where the combination of theory, numerics, and modeling can be a challenge. Includes a comprehensive survey of modern reduced-order models, including ones for data assimilation Includes a self-contained coverage of mathematical analysis of fluid flows, which will act as an ideal introduction to the book for readers without mathematical backgrounds Presents methods and techniques in a practical way so they can be rapidly applied to the reader’s own work
Book Synopsis Optimal Control of Partial Differential Equations by : Andrea Manzoni
Download or read book Optimal Control of Partial Differential Equations written by Andrea Manzoni and published by Springer Nature. This book was released on 2022-01-01 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book on optimal control problems (OCPs) for partial differential equations (PDEs) that evolved from a series of courses taught by the authors in the last few years at Politecnico di Milano, both at the undergraduate and graduate levels. The book covers the whole range spanning from the setup and the rigorous theoretical analysis of OCPs, the derivation of the system of optimality conditions, the proposition of suitable numerical methods, their formulation, their analysis, including their application to a broad set of problems of practical relevance. The first introductory chapter addresses a handful of representative OCPs and presents an overview of the associated mathematical issues. The rest of the book is organized into three parts: part I provides preliminary concepts of OCPs for algebraic and dynamical systems; part II addresses OCPs involving linear PDEs (mostly elliptic and parabolic type) and quadratic cost functions; part III deals with more general classes of OCPs that stand behind the advanced applications mentioned above. Starting from simple problems that allow a “hands-on” treatment, the reader is progressively led to a general framework suitable to face a broader class of problems. Moreover, the inclusion of many pseudocodes allows the reader to easily implement the algorithms illustrated throughout the text. The three parts of the book are suitable to readers with variable mathematical backgrounds, from advanced undergraduate to Ph.D. levels and beyond. We believe that applied mathematicians, computational scientists, and engineers may find this book useful for a constructive approach toward the solution of OCPs in the context of complex applications.
Book Synopsis Parallel Scientific Computation by : Rob H. Bisseling
Download or read book Parallel Scientific Computation written by Rob H. Bisseling and published by Oxford University Press, USA. This book was released on 2020-09-30 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel Scientific Computation presents a methodology for designing parallel algorithms and writing parallel computer programs for modern computer architectures with multiple processors.
Book Synopsis Electrical Impedance Tomography by : Andy Adler
Download or read book Electrical Impedance Tomography written by Andy Adler and published by CRC Press. This book was released on 2021-12-19 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: With contributions from leading international researchers, this second edition of Electrical Impedance Tomography: Methods, History and Applications has been fully updated throughout and contains new developments in the field, including sections on image interpretation and image reconstruction. Providing a thorough review of the progress of EIT, the present state of knowledge, and a look at future advances and applications, this accessible reference will be invaluable for mathematicians, physicists dealing with bioimpedance, electronic engineers involved in developing and extending its applications, and clinicians wishing to take advantage of this powerful imaging method. Key Features: Fully updated throughout, with new sections on image interpretation and image reconstruction Overview of the current state of experimental and clinical use of EIT as well as active research developments Overview of related research in geophysics, industrial process tomography, magnetic-resonance and magnetic-induction impedance imaging
Book Synopsis Inverse Problems and Data Assimilation by : Daniel Sanz-Alonso
Download or read book Inverse Problems and Data Assimilation written by Daniel Sanz-Alonso and published by Cambridge University Press. This book was released on 2023-08-10 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: A clear and concise mathematical introduction to the subjects of inverse problems and data assimilation, and their inter-relations.
Book Synopsis Materials and Technologies for a Green Environment by : Santhanam Harikrishnan
Download or read book Materials and Technologies for a Green Environment written by Santhanam Harikrishnan and published by Bentham Science Publishers. This book was released on 2023-03-23 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world faces significant challenges as the population and consumption continue to grow while fossil fuels and other raw materials are depleted at everincreasing rates. Environmental consciousness and a penchant for thinking about material cycles have caught on with consumers. Therefore, the use of environmentally compatible materials and sustainable production methods are now desired. Materials and Technologies for a Green Environment discusses the major issues surrounding the production of energy through biofuels and waste management. It comprises seven chapters that cover various fields of interest to readers involved in environmental management and sustainability planning. The topics covered include renewable energy sources, thermoelectric generators, electric vehicles, biodiesel production from poultry waste, scramjet combustion engines, and sustainable architecture for green buildings. Given its scope, this book is a valuable resource for students, researchers and engineers in environmental science, mechanical engineering, and chemical engineering and sustainability studies
Book Synopsis Mathematical Aspects of Deep Learning by : Philipp Grohs
Download or read book Mathematical Aspects of Deep Learning written by Philipp Grohs and published by Cambridge University Press. This book was released on 2022-12-22 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.
