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Machine Learning Control Taming Nonlinear Dynamics And Turbulence
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Book Synopsis Machine Learning Control – Taming Nonlinear Dynamics and Turbulence by : Thomas Duriez
Download or read book Machine Learning Control – Taming Nonlinear Dynamics and Turbulence written by Thomas Duriez and published by Springer. This book was released on 2016-11-02 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning Control – Taming Nonlinear Dynamics and Turbulence by : Thomas Duriez
Download or read book Machine Learning Control – Taming Nonlinear Dynamics and Turbulence written by Thomas Duriez and published by Springer. This book was released on 2016-07-28 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton
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®.
Book Synopsis Machine Learning Control by Symbolic Regression by : Askhat Diveev
Download or read book Machine Learning Control by Symbolic Regression written by Askhat Diveev and published by Springer Nature. This book was released on 2021-10-23 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting; the search for new laws in economics, politics, sociology. Accumulating many years of experience in the development and application of numerical methods of symbolic regression to solving control problems, the authors offer new possibilities not only in the field of control automation, but also in the design of completely different optimal structures in many fields. For specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints scientists with the methods of automatic construction of control systems.For specialists in the field of machine learning, the book opens up a new, much broader direction than neural networks: methods of symbolic regression. This book makes it easy to master this new area in machine learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction of numerical methods for obtaining analytical solutions to unsolvable problems; for example, numerical analytical solutions of algebraic equations, differential equations, non-trivial integrals, etc. For specialists in the field of artificial intelligence, the book offers a machine way to solve problems, framed in the form of analytical relationships.
Book Synopsis Dynamic Mode Decomposition by : J. Nathan Kutz
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.
Download or read book Out Of Control written by Kevin Kelly and published by Basic Books. This book was released on 2009-04-30 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: Out of Control chronicles the dawn of a new era in which the machines and systems that drive our economy are so complex and autonomous as to be indistinguishable from living things.
Book Synopsis Fluid-Structure-Sound Interactions and Control by : Yu Zhou
Download or read book Fluid-Structure-Sound Interactions and Control written by Yu Zhou and published by Springer. This book was released on 2018-05-15 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the Symposium on Fluid-Structure-Sound Interactions and Control (FSSIC), (held in Tokyo on Aug. 21-24, 2017), which largely focused on advances in the theory, experiments on, and numerical simulation of turbulence in the contexts of flow-induced vibration, noise and their control. This includes several practical areas of application, such as the aerodynamics of road and space vehicles, marine and civil engineering, nuclear reactors and biomedical science, etc. Uniquely, these proceedings integrate acoustics with the study of flow-induced vibration, which is not a common practice but can be extremely beneficial to understanding, simulating and controlling vibration. The symposium provides a vital forum where academics, scientists and engineers working in all related branches can exchange and share their latest findings, ideas and innovations – bringing together researchers from both east and west to chart the frontiers of FSSIC.
Download or read book Transient Chaos written by Ying-Cheng Lai and published by Springer Science & Business Media. This book was released on 2011-02-26 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this Book is to give an overview, based on the results of nearly three decades of intensive research, of transient chaos. One belief that motivates us to write this book is that, transient chaos may not have been appreciated even within the nonlinear-science community, let alone other scientific disciplines.
Book Synopsis Data-Driven Modeling & Scientific Computation by : Jose Nathan Kutz
Download or read book Data-Driven Modeling & Scientific Computation written by Jose Nathan Kutz and published by . 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.
Book Synopsis Flow Control by : Mohamed Gad-el-Hak
Download or read book Flow Control written by Mohamed Gad-el-Hak and published by Cambridge University Press. This book was released on 2000-08-15 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thorough treatment of the basics of flow control and flow control practices.
Book Synopsis Knowledge Guided Machine Learning by : Anuj Karpatne
Download or read book Knowledge Guided Machine Learning written by Anuj Karpatne and published by CRC Press. This book was released on 2022-08-15 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Book Synopsis Advances in Critical Flow Dynamics Involving Moving/Deformable Structures with Design Applications by : Marianna Braza
Download or read book Advances in Critical Flow Dynamics Involving Moving/Deformable Structures with Design Applications written by Marianna Braza and published by Springer Nature. This book was released on 2021-02-10 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports on the latest knowledge concerning critical phenomena arising in fluid-structure interaction due to movement and/or deformation of bodies. The focus of the book is on reporting progress in understanding turbulence and flow control to improve aerodynamic / hydrodynamic performance by reducing drag, increasing lift or thrust and reducing noise under critical conditions that may result in massive separation, strong vortex dynamics, amplification of harmful instabilities (flutter, buffet), and flow -induced vibrations. Theory together with large-scale simulations and experiments have revealed new features of turbulent flow in the boundary layer over bodies and in thin shear layers immediately downstream of separation. New insights into turbulent flow interacting with actively deformable structures, leading to new ways of adapting and controlling the body shape and vibrations to respond to these critical conditions, are investigated. The book covers new features of turbulent flows in boundary layers over wings and in shear layers immediately downstream: studies of natural and artificially generated fluctuations; reduction of noise and drag; and electromechanical conversion topics. Smart actuators as well as how smart designs lead to considerable benefits compared with conventional methods are also extensively discussed. Based on contributions presented at the IUTAM Symposium “Critical Flow Dynamics involving Moving/Deformable Structures with Design applications”, held in June 18-22, 2018, in Santorini, Greece, the book provides readers with extensive information about current theories, methods and challenges in flow and turbulence control, and practical knowledge about how to use this information together with smart and bio-inspired design tools to improve aerodynamic and hydrodynamic design and safety.
Book Synopsis Nonlinear Dynamics And Chaos by : Nicholas B. Tufillaro
Download or read book Nonlinear Dynamics And Chaos written by Nicholas B. Tufillaro and published by Addison Wesley Publishing Company. This book was released on 1992-05-20 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This essential handbook provides the theoretical and experimental tools necessary to begin researching the nonlinear behavior of mechanical, electrical, optical, and other systems. The book describes several nonlinear systems which are realized by desktop experiments, such as an apparatus showing chaotic string vibrations, an LRC circuit displaying strange scrolling patterns, and a bouncing ball machine illustrating the period doubling route to chaos. Fractal measures, periodic orbit extraction, and symbolic analysis are applied to unravel the chaotic motions of these systems. The simplicity of the examples makes this an excellent book for undergraduate and graduate-level physics and mathematics courses, new courses in dynamical systems, and experimental laboratories.
Book Synopsis Neural Engineering by : Chris Eliasmith
Download or read book Neural Engineering written by Chris Eliasmith and published by MIT Press. This book was released on 2003 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
Book Synopsis Advances in Effective Flow Separation Control for Aircraft Drag Reduction by : Ning Qin
Download or read book Advances in Effective Flow Separation Control for Aircraft Drag Reduction written by Ning Qin and published by Springer Nature. This book was released on 2019-10-17 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the results of a European-Chinese collaborative research project, Manipulation of Reynolds Stress for Separation Control and Drag Reduction (MARS), including an analysis and discussion of the effects of a number of active flow control devices on the discrete dynamic components of the turbulent shear layers and Reynolds stress. From an application point of view, it provides a positive and necessary step to control individual structures that are larger in scale and lower in frequency compared to the richness of the temporal and spatial scales in turbulent separated flows.
Book Synopsis Fluids Under Control by : Tomáš Bodnár
Download or read book Fluids Under Control written by Tomáš Bodnár and published by Springer Nature. This book was released on with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop
Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.