Data-driven modeling and optimization in fluid dynamics: From physics-based to machine learning approaches
Author: Michel Bergmann
Publisher: Frontiers Media SA
Published: 2023-01-05
Total Pages: 178
ISBN-13: 2832510701
DOWNLOAD EBOOK →Lanterna Rally
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Data-Driven Fluid Mechanics
Author: Miguel A. Mendez
Publisher: Cambridge University Press
Published: 2022-12-31
Total Pages: 470
ISBN-13: 110890226X
DOWNLOAD EBOOK →Data-driven methods have become an essential part of the methodological portfolio of fluid dynamicists, motivating students and practitioners to gather practical knowledge from a diverse range of disciplines. These fields include computer science, statistics, optimization, signal processing, pattern recognition, nonlinear dynamics, and control. Fluid mechanics is historically a big data field and offers a fertile ground for developing and applying data-driven methods, while also providing valuable shortcuts, constraints, and interpretations based on its powerful connections to basic physics. Thus, hybrid approaches that leverage both methods based on data as well as fundamental principles are the focus of active and exciting research. Originating from a one-week lecture series course by the von Karman Institute for Fluid Dynamics, this book presents an overview and a pedagogical treatment of some of the data-driven and machine learning tools that are leading research advancements in model-order reduction, system identification, flow control, and data-driven turbulence closures.
Data-Driven Science and Engineering
Author: Steven L. Brunton
Publisher: Cambridge University Press
Published: 2022-05-05
Total Pages: 615
ISBN-13: 1009098489
DOWNLOAD EBOOK →A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Dynamic Mode Decomposition
Author: J. Nathan Kutz
Publisher: SIAM
Published: 2016-11-23
Total Pages: 241
ISBN-13: 1611974496
DOWNLOAD EBOOK →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.
Data-Driven Modeling, Filtering and Control
Author: Carlo Novara
Publisher: Control, Robotics and Sensors
Published: 2019-09
Total Pages: 300
ISBN-13: 1785617125
DOWNLOAD EBOOK →Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.
Data-Driven Modeling & Scientific Computation
Author: J. Nathan Kutz
Publisher: Oxford University Press
Published: 2013-08-08
Total Pages: 657
ISBN-13: 0199660336
DOWNLOAD EBOOK →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.
Collection of Papers
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence
Author: Thomas Duriez
Publisher: Springer
Published: 2016-11-02
Total Pages: 211
ISBN-13: 3319406248
DOWNLOAD EBOOK →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.
Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines
Author: Jihad Badra
Publisher: Elsevier
Published: 2022-01-05
Total Pages: 262
ISBN-13: 032388458X
DOWNLOAD EBOOK →Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design. Provides AI/ML and data driven optimization techniques in combination with Computational Fluid Dynamics (CFD) to optimize engine combustion systems Features a comprehensive overview of how AI/ML techniques are used in conjunction with simulations and experiments Discusses data driven optimization techniques for fuel formulations and vehicle control calibration
The Fourth Paradigm
Author: Anthony J. G. Hey
Publisher:
Published: 2009
Total Pages: 292
ISBN-13:
DOWNLOAD EBOOK →Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication.
