Differential Privacy For Dynamic Data

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Differential Privacy for Dynamic Data

Author : Jerome Le Ny
Publisher : Springer Nature
ISBN 13 : 3030410390
Total Pages : 118 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Differential Privacy for Dynamic Data by : Jerome Le Ny

Download or read book Differential Privacy for Dynamic Data written by Jerome Le Ny and published by Springer Nature. This book was released on 2020-03-24 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.

The Algorithmic Foundations of Differential Privacy

Author : Cynthia Dwork
Publisher :
ISBN 13 : 9781601988188
Total Pages : 286 pages
Book Rating : 4.9/5 (881 download)

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Book Synopsis The Algorithmic Foundations of Differential Privacy by : Cynthia Dwork

Download or read book The Algorithmic Foundations of Differential Privacy written by Cynthia Dwork and published by . This book was released on 2014 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.

Differential Privacy

Author : Ninghui Li
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627052976
Total Pages : 140 pages
Book Rating : 4.6/5 (27 download)

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Book Synopsis Differential Privacy by : Ninghui Li

Download or read book Differential Privacy written by Ninghui Li and published by Morgan & Claypool Publishers. This book was released on 2016-10-26 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks. This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations. We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it. The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.

Dynamic Data Analysis

Author : James Ramsay
Publisher : Springer
ISBN 13 : 1493971905
Total Pages : 242 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Dynamic Data Analysis by : James Ramsay

Download or read book Dynamic Data Analysis written by James Ramsay and published by Springer. This book was released on 2017-06-27 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap.

Introducing Differential Privacy Mechanisms for Mobile App Analytics of Dynamic Content

Author : Sufian Latif
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Introducing Differential Privacy Mechanisms for Mobile App Analytics of Dynamic Content by : Sufian Latif

Download or read book Introducing Differential Privacy Mechanisms for Mobile App Analytics of Dynamic Content written by Sufian Latif and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile app analytics gathers detailed data about millions of app users. Both customers and governments are becoming increasingly concerned about the privacy implications of such data gathering. Thus, it is highly desirable to design privacy-preserving versions of mobile app analytics. We aim to achieve this goal using differential privacy, a leading algorithm design framework for privacy-preserving data analysis. We apply differential privacy to dynamically-created content that is retrieved from a content server and is displayed to the app user. User interactions with this content are then reported to the app analytics infrastructure. Unlike problems considered in related prior work, such analytics could potentially convey a wealth of sensitive information---for example, about an app user's political beliefs, dietary choices, health conditions, or travel interests. To provide rigorous privacy protections for this information, we design a differentially-private solution for such data gathering. Our first contribution is a differentially-private scheme for mobile app analytics of such content. We first present a conceptual design for this data collection.

Hands-On Differential Privacy: Introduction to the Theory and Practice Using Opendp

Author : Ethan Cowan
Publisher : O'Reilly Media
ISBN 13 : 9781492097747
Total Pages : 0 pages
Book Rating : 4.0/5 (977 download)

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Book Synopsis Hands-On Differential Privacy: Introduction to the Theory and Practice Using Opendp by : Ethan Cowan

Download or read book Hands-On Differential Privacy: Introduction to the Theory and Practice Using Opendp written by Ethan Cowan and published by O'Reilly Media. This book was released on 2024-04-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Mayana Pereira, and Michael Shoemate explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows. With this book, you'll learn: How DP guarantees privacy when other data anonymization methods don't What preserving individual privacy in a dataset entails How to apply DP in several real-world scenarios and datasets Potential privacy attack methods, including what it means to perform a reidentification attack How to use the OpenDP library in privacy-preserving data releases How to interpret guarantees provided by specific DP data releases

Handbook on Using Administrative Data for Research and Evidence-based Policy

Author : Shawn Cole
Publisher : Abdul Latif Jameel Poverty Action Lab
ISBN 13 : 9781736021606
Total Pages : 618 pages
Book Rating : 4.0/5 (216 download)

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Book Synopsis Handbook on Using Administrative Data for Research and Evidence-based Policy by : Shawn Cole

Download or read book Handbook on Using Administrative Data for Research and Evidence-based Policy written by Shawn Cole and published by Abdul Latif Jameel Poverty Action Lab. This book was released on 2021 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook intends to inform Data Providers and researchers on how to provide privacy-protected access to, handle, and analyze administrative data, and to link them with existing resources, such as a database of data use agreements (DUA) and templates. Available publicly, the Handbook will provide guidance on data access requirements and procedures, data privacy, data security, property rights, regulations for public data use, data architecture, data use and storage, cost structure and recovery, ethics and privacy-protection, making data accessible for research, and dissemination for restricted access use. The knowledge base will serve as a resource for all researchers looking to work with administrative data and for Data Providers looking to make such data available.

Differential Privacy and Applications

Author : Tianqing Zhu
Publisher : Springer
ISBN 13 : 3319620045
Total Pages : 243 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Differential Privacy and Applications by : Tianqing Zhu

Download or read book Differential Privacy and Applications written by Tianqing Zhu and published by Springer. This book was released on 2017-08-22 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.

Privacy-Preserving Data Publishing

Author : Bee-Chung Chen
Publisher : Now Publishers Inc
ISBN 13 : 1601982763
Total Pages : 183 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Privacy-Preserving Data Publishing by : Bee-Chung Chen

Download or read book Privacy-Preserving Data Publishing written by Bee-Chung Chen and published by Now Publishers Inc. This book was released on 2009-10-14 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is dedicated to those who have something to hide. It is a book about "privacy preserving data publishing" -- the art of publishing sensitive personal data, collected from a group of individuals, in a form that does not violate their privacy. This problem has numerous and diverse areas of application, including releasing Census data, search logs, medical records, and interactions on a social network. The purpose of this book is to provide a detailed overview of the current state of the art as well as open challenges, focusing particular attention on four key themes: RIGOROUS PRIVACY POLICIES Repeated and highly-publicized attacks on published data have demonstrated that simplistic approaches to data publishing do not work. Significant recent advances have exposed the shortcomings of naive (and not-so-naive) techniques. They have also led to the development of mathematically rigorous definitions of privacy that publishing techniques must satisfy; METRICS FOR DATA UTILITY While it is necessary to enforce stringent privacy policies, it is equally important to ensure that the published version of the data is useful for its intended purpose. The authors provide an overview of diverse approaches to measuring data utility; ENFORCEMENT MECHANISMS This book describes in detail various key data publishing mechanisms that guarantee privacy and utility; EMERGING APPLICATIONS The problem of privacy-preserving data publishing arises in diverse application domains with unique privacy and utility requirements. The authors elaborate on the merits and limitations of existing solutions, based on which we expect to see many advances in years to come.

Handbook of Dynamic Data Driven Applications Systems

Author : Erik P. Blasch
Publisher : Springer Nature
ISBN 13 : 3030745686
Total Pages : 753 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Handbook of Dynamic Data Driven Applications Systems by : Erik P. Blasch

Download or read book Handbook of Dynamic Data Driven Applications Systems written by Erik P. Blasch and published by Springer Nature. This book was released on 2022-05-11 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies. Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in ten application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal: The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination. The Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms. Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions. In so doing, all resources are used in an optimal manner to maximize the quality and timeliness of information they provide. Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential. Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University

Handbook of Dynamic Data Driven Applications Systems

Author : Erik Blasch
Publisher : Springer
ISBN 13 : 3319955047
Total Pages : 750 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Handbook of Dynamic Data Driven Applications Systems by : Erik Blasch

Download or read book Handbook of Dynamic Data Driven Applications Systems written by Erik Blasch and published by Springer. This book was released on 2018-11-13 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies. Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in10 application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal: Earth and Space Data Assimilation Aircraft Systems Processing Structures Health Monitoring Biological Data Assessment Object and Activity Tracking Embedded Control and Coordination Energy-Aware Optimization Image and Video Computing Security and Policy Coding Systems Design The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination.

LQ Dynamic Optimization and Differential Games

Author : Jacob Engwerda
Publisher : John Wiley & Sons
ISBN 13 : 9780470015247
Total Pages : 514 pages
Book Rating : 4.0/5 (152 download)

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Book Synopsis LQ Dynamic Optimization and Differential Games by : Jacob Engwerda

Download or read book LQ Dynamic Optimization and Differential Games written by Jacob Engwerda and published by John Wiley & Sons. This book was released on 2005-06-17 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Game theory is the theory of social situations, and the majority of research into the topic focuses on how groups of people interact by developing formulas and algorithms to identify optimal strategies and to predict the outcome of interactions. Only fifty years old, it has already revolutionized economics and finance, and is spreading rapidly to a wide variety of fields. LQ Dynamic Optimization and Differential Games is an assessment of the state of the art in its field and the first modern book on linear-quadratic game theory, one of the most commonly used tools for modelling and analysing strategic decision making problems in economics and management. Linear quadratic dynamic models have a long tradition in economics, operations research and control engineering; and the author begins by describing the one-decision maker LQ dynamic optimization problem before introducing LQ differential games. Covers cooperative and non-cooperative scenarios, and treats the standard information structures (open-loop and feedback). Includes real-life economic examples to illustrate theoretical concepts and results. Presents problem formulations and sound mathematical problem analysis. Includes exercises and solutions, enabling use for self-study or as a course text. Supported by a website featuring solutions to exercises, further examples and computer code for numerical examples. LQ Dynamic Optimization and Differential Games offers a comprehensive introduction to the theory and practice of this extensively used class of economic models, and will appeal to applied mathematicians and econometricians as well as researchers and senior undergraduate/graduate students in economics, mathematics, engineering and management science.

Privacy-Preserving Data Mining

Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
ISBN 13 : 0387709924
Total Pages : 524 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Privacy-Preserving Data Mining by : Charu C. Aggarwal

Download or read book Privacy-Preserving Data Mining written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2008-06-10 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.

Linking Sensitive Data

Author : Peter Christen
Publisher :
ISBN 13 : 3030597067
Total Pages : 476 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Linking Sensitive Data by : Peter Christen

Download or read book Linking Sensitive Data written by Peter Christen and published by . This book was released on 2020 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides modern technical answers to the legal requirements of pseudonymisation as recommended by privacy legislation. It covers topics such as modern regulatory frameworks for sharing and linking sensitive information, concepts and algorithms for privacy-preserving record linkage and their computational aspects, practical considerations such as dealing with dirty and missing data, as well as privacy, risk, and performance assessment measures. Existing techniques for privacy-preserving record linkage are evaluated empirically and real-world application examples that scale to population sizes are described. The book also includes pointers to freely available software tools, benchmark data sets, and tools to generate synthetic data that can be used to test and evaluate linkage techniques. This book consists of fourteen chapters grouped into four parts, and two appendices. The first part introduces the reader to the topic of linking sensitive data, the second part covers methods and techniques to link such data, the third part discusses aspects of practical importance, and the fourth part provides an outlook of future challenges and open research problems relevant to linking sensitive databases. The appendices provide pointers and describe freely available, open-source software systems that allow the linkage of sensitive data, and provide further details about the evaluations presented. A companion Web site at https://dmm.anu.edu.au/lsdbook2020 provides additional material and Python programs used in the book. This book is mainly written for applied scientists, researchers, and advanced practitioners in governments, industry, and universities who are concerned with developing, implementing, and deploying systems and tools to share sensitive information in administrative, commercial, or medical databases. The Book describes how linkage methods work and how to evaluate their performance. It covers all the major concepts and methods and also discusses practical matters such as computational efficiency, which are critical if the methods are to be used in practice - and it does all this in a highly accessible way! David J. Hand, Imperial College, London.

Dynamic Optimization and Differential Games

Author : Terry L. Friesz
Publisher : Springer Science & Business Media
ISBN 13 : 0387727787
Total Pages : 509 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Dynamic Optimization and Differential Games by : Terry L. Friesz

Download or read book Dynamic Optimization and Differential Games written by Terry L. Friesz and published by Springer Science & Business Media. This book was released on 2010-08-20 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has been written to address the increasing number of Operations Research and Management Science problems (that is, applications) that involve the explicit consideration of time and of gaming among multiple agents. It is a book that will be used both as a textbook and as a reference and guide by those whose work involves the theoretical aspects of dynamic optimization and differential games.

Functional Data Analysis

Author : James Ramsay
Publisher : Springer Science & Business Media
ISBN 13 : 147577107X
Total Pages : 317 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Functional Data Analysis by : James Ramsay

Download or read book Functional Data Analysis written by James Ramsay and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Included here are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drawn from growth analysis, meteorology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, applied data analysts, and to experienced researchers; and as such is of value both within statistics and across a broad spectrum of other fields. Much of the material appears here for the first time.

Dynamic Mode Decomposition

Author : J. Nathan Kutz
Publisher : SIAM
ISBN 13 : 1611974496
Total Pages : 241 pages
Book Rating : 4.6/5 (119 download)

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Book Synopsis Dynamic Mode Decomposition by : J. Nathan Kutz

Download or read book Dynamic Mode Decomposition written by J. Nathan Kutz and published by SIAM. This book was released on 2016-11-23 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.