Data Driven Fluid Mechanics
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Data Driven Fluid Mechanics
Author | : Miguel A. Mendez,Andrea Ianiro,Bernd R. Noack,Steven L. Brunton |
Publsiher | : Cambridge University Press |
Total Pages | : 469 |
Release | : 2023-01-31 |
Genre | : Science |
ISBN | : 9781108842143 |
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This is the first book dedicated to data-driven methods for fluid dynamics, with applications in analysis, modeling, control, and closures.
Data Driven Science and Engineering
Author | : Steven L. Brunton,J. Nathan Kutz |
Publsiher | : Cambridge University Press |
Total Pages | : 615 |
Release | : 2022-05-05 |
Genre | : Computers |
ISBN | : 9781009098489 |
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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Experimental Aerodynamics
Author | : Stefano Discetti,Andrea Ianiro |
Publsiher | : CRC Press |
Total Pages | : 454 |
Release | : 2017-03-16 |
Genre | : Technology & Engineering |
ISBN | : 9781498704021 |
Download Experimental Aerodynamics Book in PDF, Epub and Kindle
Experimental Aerodynamics provides an up to date study of this key area of aeronautical engineering. The field has undergone significant evolution with the development of 3D techniques, data processing methods, and the conjugation of simultaneous measurements of multiple quantities. Written for undergraduate and graduate students in Aerospace Engineering, the text features chapters by leading experts, with a consistent structure, level, and pedagogical approach. Fundamentals of measurements and recent research developments are introduced, supported by numerous examples, illustrations, and problems. The text will also be of interest to those studying mechanical systems, such as wind turbines.
Machine Learning Control Taming Nonlinear Dynamics and Turbulence
Author | : Thomas Duriez,Steven L. Brunton,Bernd R. Noack |
Publsiher | : Springer |
Total Pages | : 211 |
Release | : 2016-11-02 |
Genre | : Technology & Engineering |
ISBN | : 9783319406244 |
Download Machine Learning Control Taming Nonlinear Dynamics and Turbulence Book in PDF, Epub and Kindle
This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.
On the Data driven Reduced Order Modelling in Fluid Dynamics
Author | : Antonio Colanera |
Publsiher | : Unknown |
Total Pages | : 0 |
Release | : 2024 |
Genre | : Science |
ISBN | : 9791222726465 |
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Dynamic Mode Decomposition
Author | : J. Nathan Kutz,Steven L. Brunton,Bingni W. Brunton,Joshua L. Proctor |
Publsiher | : SIAM |
Total Pages | : 241 |
Release | : 2016-11-23 |
Genre | : Science |
ISBN | : 9781611974492 |
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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.
Two Lectures
Author | : Werner Heisenberg |
Publsiher | : CUP Archive |
Total Pages | : 64 |
Release | : 1949 |
Genre | : Electric conductivity |
ISBN | : 9182736450XXX |
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Data Driven Modeling Scientific Computation
Author | : J. Nathan Kutz |
Publsiher | : Oxford University Press |
Total Pages | : 657 |
Release | : 2013-08-08 |
Genre | : Computers |
ISBN | : 9780199660339 |
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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.