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®.

Download or read book Data Driven Modeling For Analysis And Control Of Dynamical Systems written by Damien Gueho and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation advances the understanding of data-driven modeling and delivers tools to pursue the ambition of complete unsupervised identification of dynamical systems. From measured data only, the proposed framework consists of a series of modules to derive accurate mathematical models for the state prediction of a wide range of linear and nonlinear dynamical systems. Identified models are constructed to be of low complexity and amenable for analysis and control. This developed framework provides a unified mathematical structure for the identification of nonlinear systems based on the Koopman operator. A main contribution of this dissertation is to introduce the concept of time-varying Koopman operator for accurate modeling of dynamical systems in a given domain around a reference trajectory. Subspace identification methods coupled with sparse approximation techniques deliver accurate models both in the continuous and discrete time domains. This allows for perfect reconstruction of several classes of nonlinear dynamical systems, from the chaotic behavior of the Lorenz oscillator to identifying the Newton's law of gravitation. The connection between the Koopman operator and higher-order state transition matrices (STMs) is explicitly discussed. It is shown that subspace methods based on the Koopman operator are able to accurately identify the linear time varying model for the propagation of higher order STMs when polynomial basis are used as lifting functions. Such algorithms are validated on a wide range of nonlinear dynamical systems of varying complexity and are proven to be very effective on nonlinear systems of higher dimension where traditional methods either fail or perform poorly. Applications include model-order reduction in hypersonic aerothermoelasticity and reduced-order dynamics in a high-dimensional finite-element model of the Von Kàrmàn Beam. Numerical simulation results confirm better prediction accuracy by several orders of magnitude using this framework. Additionally, a major objective of this research is to enhance the field of data-driven uncertainty quantification for nonlinear dynamical systems. Uncertainty propagation through nonlinear dynamics is computationally expensive. Conventional approaches focus on finding a reduced order model to alleviate the computational complexity associated with the uncertainty propagation algorithms. This dissertation exploits the fact that the moment propagation equations form a linear time-varying (LTV) system and use system theory to identify this LTV system from data only. By estimating and propagating higher-order moments of an initial probability density function, two new approaches are presented and compared to analytical and quadrature-based methods for estimating the uncertainty associated with a system's states. In all test cases considered in this dissertation, a newly-introduced indirect method using a time-varying subspace identification technique jointly with a quadrature method achieved the best results. This dissertation also extends the Koopman operator theoretic framework for controlled dynamical systems and offers a global overview of bilinear system identification techniques as well as perspectives and advances for bilinear system identification. Nonlinear dynamics with a control action are approximated as a bilinear system in a higher-dimensional space, leading to increased accuracy in the prediction of the system's response. In the same context, a data-driven parameter sensitivity method is developed using bilinear system identification algorithms. Finally, this dissertation investigates new ways to alleviate the effect of noise in the data, leading to new algorithms with data-correlations and rank optimization for optimal subspace identification.

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 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.

Download or read book Data Driven Strategies written by Wang Jianhong and published by CRC Press. This book was released on 2023-03-31 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.

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 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Driven Modeling Filtering and Control written by Carlo Novara and published by Control, Robotics and Sensors. This book was released on 2019-09 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Modeling Analysis And Control Of Dynamical Systems With Friction And Impacts written by Pawel Olejnik and published by #N/A. This book was released on 2017-07-07 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed primarily towards physicists and mechanical engineers specializing in modeling, analysis, and control of discontinuous systems with friction and impacts. It fills a gap in the existing literature by offering an original contribution to the field of discontinuous mechanical systems based on mathematical and numerical modeling as well as the control of such systems. Each chapter provides the reader with both the theoretical background and results of verified and useful computations, including solutions of the problems of modeling and application of friction laws in numerical computations, results from finding and analyzing impact solutions, the analysis and control of dynamical systems with discontinuities, etc. The contents offer a smooth correspondence between science and engineering and will allow the reader to discover new ideas. Also emphasized is the unity of diverse branches of physics and mathematics towards understanding complex piecewise-smooth dynamical systems. Mathematical models presented will be important in numerical experiments, experimental measurements, and optimization problems found in applied mechanics.

Download or read book System and Data Driven Methods and Algorithms written by Peter Benner and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-11-08 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.

Download or read book Automating Data Driven Modelling of Dynamical Systems written by Dhruv Khandelwal and published by Springer Nature. This book was released on 2022-02-03 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user’s perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.

Download or read book Data Driven Fluid Mechanics written by Miguel A. Mendez and published by Cambridge University Press. This book was released on 2023-01-31 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book dedicated to data-driven methods for fluid dynamics, with applications in analysis, modeling, control, and closures.

Download or read book Deep Data driven Modeling and Control of High dimensional Nonlinear Systems written by Jeremy Green Morton and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to derive models for dynamical systems is a central focus in many realms of science and engineering. However, for many systems of interest, the governing equations are either unknown or can only be evaluated to high accuracy at significant computational expense. Difficulties with modeling can be further exacerbated by additional complexities, such as high-dimensional states or nonlinearities in the dynamics. In turn, these challenges can hinder performance on important downstream tasks, such as prediction and control. This thesis presents techniques for learning dynamics models from data. By taking a data-driven approach, models can be derived even for systems with governing equations that are unknown or expensive to evaluate. Furthermore, training procedures can be tailored to provide learned models with desirable properties, such as low dimensionality (for efficient evaluation and storage) or linearity (for control). The proposed techniques are primarily evaluated on their ability to learn from data generated by computational fluid dynamics (CFD) simulations. CFD data serves as an ideal test case for data-driven techniques because the simulated fluid flows are nonlinear and can exhibit a wide array of behaviors. Additionally, modeling and even storage of CFD data can prove challenging due to the large number of degrees of freedom in many simulations, which can cause time snapshots of the flow field to contain megabytes or even gigabytes of data. First, this thesis proposes a multi-stage compression procedure to alleviate the storage overhead associated with running large-scale CFD simulations. Individual time snapshots are compressed through a combination of neural network autoencoders and principal component analysis. Subsequently, a dynamics model is learned that can faithfully propagate the compressed representations in time. The proposed method is able to compress the stored data by a factor of over a million, while still allowing for accurate reconstruction of all flow solutions at all time instances. The high computational cost of CFD simulations can make it impractical to run large numbers of simulations at diverse flow conditions. The second part of this thesis introduces a method for performing generative modeling, which allows for the efficient simulation of fluid flows at a wide range of flow conditions given data from only a subset of those conditions. The proposed method, which relies upon techniques from variational inference, is shown to generate accurate simulations at a range of conditions for both two- and three-dimensional fluid flow problems. The equations that govern fluid flow are nonlinear, meaning that many control techniques, largely derived for linear systems, prove ineffective when applied to fluid flow control. This thesis proposes a method, grounded in Koopman theory, for discovering data-driven linear models that can approximate the forced dynamics of systems with nonlinear dynamics. The method is shown to produce stable dynamics models that can accurately predict the time evolution of airflow over a cylinder. Furthermore, by performing model predictive control with the learned models, a straightforward, interpretable control law is found that is capable of suppressing vortex shedding in the cylinder wake. In the final part of this thesis, the Deep Variational Koopman (DVK) model is introduced, which is a method for inferring distributions over Koopman observations that can be propagated linearly in time. By sampling from the inferred distributions, an ensemble of dynamics models is obtained, which in turn provides a distribution over possible outcomes as a modeled system advances in time. Experiments show that the DVK model is capable of accurate, long-term prediction for a variety of dynamical systems. Furthermore, it is demonstrated that accounting for the uncertainty present in the distribution over dynamics models enables more effective control.

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.

Download or read book Dynamic Data driven Simulation Real time Data For Dynamic System Analysis And Prediction written by Xiaolin Hu and published by World Scientific. This book was released on 2023-03-21 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive book systematically introduces Dynamic Data Driven Simulation (DDDS) as a new simulation paradigm that makes real-time data and simulation model work together to enable simulation-based prediction/analysis.The text is significantly dedicated to introducing data assimilation as an enabling technique for DDDS. While data assimilation has been studied in other science fields (e.g., meteorology, oceanography), it is a new topic for the modeling and simulation community.This unique reference text bridges the two study areas of data assimilation and modelling and simulation, which have been developed largely independently from each other.

Download or read book The Koopman Operator in Systems and Control written by Alexandre Mauroy and published by Springer Nature. This book was released on 2020-02-22 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of state-of-the-art research at the intersection of the Koopman operator theory and control theory. It also reviews novel theoretical results obtained and efficient numerical methods developed within the framework of Koopman operator theory. The contributions discuss the latest findings and techniques in several areas of control theory, including model predictive control, optimal control, observer design, systems identification and structural analysis of controlled systems, addressing both theoretical and numerical aspects and presenting open research directions, as well as detailed numerical schemes and data-driven methods. Each contribution addresses a specific problem. After a brief introduction of the Koopman operator framework, including basic notions and definitions, the book explores numerical methods, such as the dynamic mode decomposition (DMD) algorithm and Arnoldi-based methods, which are used to represent the operator in a finite-dimensional basis and to compute its spectral properties from data. The main body of the book is divided into three parts: theoretical results and numerical techniques for observer design, synthesis analysis, stability analysis, parameter estimation, and identification; data-driven techniques based on DMD, which extract the spectral properties of the Koopman operator from data for the structural analysis of controlled systems; and Koopman operator techniques with specific applications in systems and control, which range from heat transfer analysis to robot control. A useful reference resource on the Koopman operator theory for control theorists and practitioners, the book is also of interest to graduate students, researchers, and engineers looking for an introduction to a novel and comprehensive approach to systems and control, from pure theory to data-driven methods.

Download or read book Data driven Science and Engineering written by Steven Lee Brunton and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.

Download or read book Computational Science ICCS 2004 written by Marian Bubak and published by Springer Science & Business Media. This book was released on 2004-05-26 with total page 1376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Conference on Computational Science (ICCS 2004) held in Krak ́ ow, Poland, June 6–9, 2004, was a follow-up to the highly successful ICCS 2003 held at two locations, in Melbourne, Australia and St. Petersburg, Russia; ICCS 2002 in Amsterdam, The Netherlands; and ICCS 2001 in San Francisco, USA. As computational science is still evolving in its quest for subjects of inves- gation and e?cient methods, ICCS 2004 was devised as a forum for scientists from mathematics and computer science, as the basic computing disciplines and application areas, interested in advanced computational methods for physics, chemistry, life sciences, engineering, arts and humanities, as well as computer system vendors and software developers. The main objective of this conference was to discuss problems and solutions in all areas, to identify new issues, to shape future directions of research, and to help users apply various advanced computational techniques. The event harvested recent developments in com- tationalgridsandnextgenerationcomputingsystems,tools,advancednumerical methods, data-driven systems, and novel application ?elds, such as complex - stems, ?nance, econo-physics and population evolution.