Verlag: Cambridge University Press, 2022
ISBN 10: 1009098489 ISBN 13: 9781009098489
Sprache: Englisch
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Verlag: Cambridge University Press, 2022
ISBN 10: 1009098489 ISBN 13: 9781009098489
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Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
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Verlag: Cambridge University Press, 2022
ISBN 10: 1009098489 ISBN 13: 9781009098489
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Verlag: Cambridge University Press, 2022
ISBN 10: 1009098489 ISBN 13: 9781009098489
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Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
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Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
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Verlag: Cambridge University Press, 2022
ISBN 10: 1009098489 ISBN 13: 9781009098489
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Verlag: Cambridge University Press, 2022
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Verlag: Cambridge University Press 2022-05-05, 2022
ISBN 10: 1009098489 ISBN 13: 9781009098489
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Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
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ISBN 10: 1009098489 ISBN 13: 9781009098489
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ISBN 10: 1108422098 ISBN 13: 9781108422093
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In den WarenkorbHardback. Zustand: New. 1st. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
Sprache: Englisch
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Verlag: Society for Industrial and Applied Mathematics,U.S., US, 2016
ISBN 10: 1611974496 ISBN 13: 9781611974492
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. 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 is the first book to address the DMD algorithm, it: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.Provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.
Verlag: Society For Industrial & Applied Mathematics,U.S., 2016
ISBN 10: 1611974496 ISBN 13: 9781611974492
Sprache: Englisch
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Verlag: Cambridge University Press CUP, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
Sprache: Englisch
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Zustand: New. Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on rei.
Verlag: Cambridge University Press, 2022
ISBN 10: 1009098489 ISBN 13: 9781009098489
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: Brand New. 2nd edition. 550 pages. 10.00x7.00x1.25 inches. In Stock.
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In den WarenkorbHardcover. Zustand: Brand New. 472 pages. 10.25x7.25x1.00 inches. In Stock.
Verlag: SIAM-Society for Industrial and Applied Mathematics, 2016
ISBN 10: 1611974496 ISBN 13: 9781611974492
Sprache: Englisch
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Verlag: Cambridge University Press, GB, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. 1st. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
Verlag: Society for Industrial and Applied Mathematics,U.S., US, 2016
ISBN 10: 1611974496 ISBN 13: 9781611974492
Sprache: Englisch
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
EUR 85,57
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In den WarenkorbPaperback. Zustand: New. 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 is the first book to address the DMD algorithm, it: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.Provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.
Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
Sprache: Englisch
Anbieter: medimops, Berlin, Deutschland
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Verlag: Society for Industrial and Applied Mathematics, 2017
ISBN 10: 1611974496 ISBN 13: 9781611974492
Sprache: Englisch
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Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
Sprache: Englisch
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Verlag: Cambridge University Press, 2019
ISBN 10: 1108422098 ISBN 13: 9781108422093
Sprache: Englisch
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