Data Driven Modeling And Optimization In Fluid Dynamics From Physics Based To Machine Learning Approaches PDF Download
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Author | : Michel Bergmann |
Publisher | : Frontiers Media SA |
Total Pages | : 178 |
Release | : 2023-01-05 |
Genre | : Science |
ISBN | : 2832510701 |
Download Data-driven modeling and optimization in fluid dynamics: From physics-based to machine learning approaches Book in PDF, ePub and Kindle
Author | : Miguel A. Mendez |
Publisher | : Cambridge University Press |
Total Pages | : 470 |
Release | : 2022-12-31 |
Genre | : Science |
ISBN | : 110890226X |
Download Data-Driven Fluid Mechanics Book in PDF, ePub and Kindle
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.
Author | : Steven L. Brunton |
Publisher | : Cambridge University Press |
Total Pages | : 615 |
Release | : 2022-05-05 |
Genre | : Computers |
ISBN | : 1009098489 |
Download Data-Driven Science and Engineering Book in PDF, ePub and Kindle
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author | : Steven L. Brunton |
Publisher | : Cambridge University Press |
Total Pages | : 616 |
Release | : 2022-05-05 |
Genre | : Computers |
ISBN | : 1009115634 |
Download Data-Driven Science and Engineering Book in PDF, ePub and Kindle
Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Author | : |
Publisher | : |
Total Pages | : 104 |
Release | : 1776 |
Genre | : |
ISBN | : |
Download Collection of Papers Book in PDF, ePub and Kindle
Author | : J. Nathan Kutz |
Publisher | : SIAM |
Total Pages | : 241 |
Release | : 2016-11-23 |
Genre | : Science |
ISBN | : 1611974496 |
Download Dynamic Mode Decomposition Book in PDF, ePub and Kindle
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 | : Carlo Novara |
Publisher | : Control, Robotics and Sensors |
Total Pages | : 300 |
Release | : 2019-09 |
Genre | : Technology & Engineering |
ISBN | : 1785617125 |
Download Data-Driven Modeling, Filtering and Control Book in PDF, ePub and Kindle
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.
Author | : J. Nathan Kutz |
Publisher | : Oxford University Press |
Total Pages | : 657 |
Release | : 2013-08-08 |
Genre | : Computers |
ISBN | : 0199660336 |
Download Data-Driven Modeling & Scientific Computation Book in PDF, ePub and Kindle
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 | : Thomas Duriez |
Publisher | : Springer |
Total Pages | : 211 |
Release | : 2016-11-02 |
Genre | : Technology & Engineering |
ISBN | : 3319406248 |
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.
Author | : Jihad Badra |
Publisher | : Elsevier |
Total Pages | : 262 |
Release | : 2022-01-05 |
Genre | : Technology & Engineering |
ISBN | : 032388458X |
Download Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines Book in PDF, ePub and Kindle
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