Data Driven Modeling Scientific Computation

Author : J. Nathan Kutz
Publisher : Oxford University Press
ISBN 13 : 0199660336
Total Pages : 657 pages
Book Rating : 4.39/5 ( download)

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Download or read book Data-Driven Modeling & Scientific Computation written by J. Nathan Kutz and published by Oxford University Press. 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.

Author : Steven L. Brunton
Publisher : Cambridge University Press
ISBN 13 : 1009098489
Total Pages : 615 pages
Book Rating : 4.89/5 ( download)

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Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Author : Ben Klemens
Publisher : Princeton University Press
ISBN 13 : 1400828740
Total Pages : 471 pages
Book Rating : 4.46/5 ( download)

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Download or read book Modeling with Data written by Ben Klemens and published by Princeton University Press. This book was released on 2008-10-06 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date. Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Author : John Harlim
Publisher : Cambridge University Press
ISBN 13 : 1108472478
Total Pages : 171 pages
Book Rating : 4.70/5 ( download)

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Download or read book Data-Driven Computational Methods written by John Harlim and published by Cambridge University Press. This book was released on 2018-07-12 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes computational methods for parametric and nonparametric modeling of stochastic dynamics. Aimed at graduate students, and suitable for self-study.

Author : J. Nathan Kutz
Publisher : SIAM
ISBN 13 : 1611974496
Total Pages : 241 pages
Book Rating : 4.92/5 ( download)

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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.

Author : Bertil Gustafsson
Publisher : Springer Science & Business Media
ISBN 13 : 3642194958
Total Pages : 326 pages
Book Rating : 4.55/5 ( download)

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Download or read book Fundamentals of Scientific Computing written by Bertil Gustafsson and published by Springer Science & Business Media. This book was released on 2011-06-11 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book of nature is written in the language of mathematics -- Galileo Galilei How is it possible to predict weather patterns for tomorrow, with access solely to today’s weather data? And how is it possible to predict the aerodynamic behavior of an aircraft that has yet to be built? The answer is computer simulations based on mathematical models – sets of equations – that describe the underlying physical properties. However, these equations are usually much too complicated to solve, either by the smartest mathematician or the largest supercomputer. This problem is overcome by constructing an approximation: a numerical model with a simpler structure can be translated into a program that tells the computer how to carry out the simulation. This book conveys the fundamentals of mathematical models, numerical methods and algorithms. Opening with a tutorial on mathematical models and analysis, it proceeds to introduce the most important classes of numerical methods, with finite element, finite difference and spectral methods as central tools. The concluding section describes applications in physics and engineering, including wave propagation, heat conduction and fluid dynamics. Also covered are the principles of computers and programming, including MATLAB®.

Author : Rob H. Bisseling
Publisher : Oxford University Press, USA
ISBN 13 : 0198788347
Total Pages : 410 pages
Book Rating : 4.48/5 ( download)

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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.

Author : Ellen Kuhl
Publisher : Springer Nature
ISBN 13 : 3030828905
Total Pages : 312 pages
Book Rating : 4.05/5 ( download)

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Download or read book Computational Epidemiology written by Ellen Kuhl and published by Springer Nature. This book was released on 2021-09-22 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: This innovative textbook brings together modern concepts in mathematical epidemiology, computational modeling, physics-based simulation, data science, and machine learning to understand one of the most significant problems of our current time, the outbreak dynamics and outbreak control of COVID-19. It teaches the relevant tools to model and simulate nonlinear dynamic systems in view of a global pandemic that is acutely relevant to human health. If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it.

Author : Concha Bielza
Publisher : Cambridge University Press
ISBN 13 : 110849370X
Total Pages : 709 pages
Book Rating : 4.03/5 ( download)

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Download or read book Data-Driven Computational Neuroscience written by Concha Bielza and published by Cambridge University Press. This book was released on 2020-11-26 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

Author : Dianne P. O'Leary
Publisher : SIAM
ISBN 13 : 0898716667
Total Pages : 376 pages
Book Rating : 4.65/5 ( download)

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Download or read book Scientific Computing with Case Studies written by Dianne P. O'Leary and published by SIAM. This book was released on 2009-03-19 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a practical guide to the numerical solution of linear and nonlinear equations, differential equations, optimization problems, and eigenvalue problems. It treats standard problems and introduces important variants such as sparse systems, differential-algebraic equations, constrained optimization, Monte Carlo simulations, and parametric studies. Stability and error analysis are emphasized, and the Matlab algorithms are grounded in sound principles of software design and understanding of machine arithmetic and memory management. Nineteen case studies provide experience in mathematical modeling and algorithm design, motivated by problems in physics, engineering, epidemiology, chemistry, and biology. The topics included go well beyond the standard first-course syllabus, introducing important problems such as differential-algebraic equations and conic optimization problems, and important solution techniques such as continuation methods. The case studies cover a wide variety of fascinating applications, from modeling the spread of an epidemic to determining truss configurations.