Download or read book Data Driven Modeling and Pattern Recognition of Dynamical Systems written by Pritthi Chattopadhyay and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Human-engineered complex systems need to be monitored consistently to ensuretheir safety and efficiency, which might be affected due to degradation over timeor unanticipated disturbances. For systems that change at a fast time scale, insteadof active health monitoring, preventative system design is more feasible andeffective. Both active health monitoring and preventative system design can bedone using physics-based or data-driven models. In comparison to physics-basedmodels, data-driven models do not require knowledge of the underlying systemdynamics; they determine the relation between the relevant input and output variablesfrom a training data set. This is useful when there is lack of understandingof the system dynamics or the developed models are inadequate. One such scenariois combustion, where the difficulties include nonlinear dynamics involvingseveral input parameters; existence of bifurcations in the dynamic behavior andextremely high sensitivity of the combustor behavior to even small changes insome of the design parameters. Similarly, for batteries, sufficient knowledge of theelectrochemical characteristics is necessary to develop models for parameter identification at different operating points of the nonlinear battery dynamics. Thisdissertation develops dynamic data-driven models for combustor design and batteryhealth monitoring, using concepts of machine learning and statistics, whichdo not require much knowledge of the underlying system dynamics.But the performance of a data-driven algorithm depends on many factors namely:1. Availability of training data which covers all events of interest. For applicationsinvolving time series data, each individual time series must also besufficiently long, to encompass the dynamics of the underlying system foreach event.2. The quality of extracted features, i.e. whether they capture all the informationabout the system.3. The relation between the relevant input and output variables remaining constantduring the time the algorithm is being trained.Hence, the second part of the dissertation develops an unsupervised algorithm forscenarios where condition (iii) might not hold; quanties the eect of the nonconformityof condition (i) on the performance of an algorithm and proposes afeature extraction algorithm to ensure conformity of condition (ii).

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 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 2022-12-31 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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 Data driven Modeling of Dynamical Systems written by Kunal Raj Menda and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots, automated decision systems, and predictive algorithms have become ubiquitous in our world, and we are becoming increasingly reliant on their ability to make intelligent decisions for us. We task these systems with choosing actions in sequential decision-making settings that will reap the best performance in the long-run, and hope to deploy them in environments about which they are uncertain. Uncertainty hampers the ability to make optimal decisions, and it arises from uncertainty about the state of the world, uncertainty about how the world changes, and uncertainty about what it means to act optimally. For a machine to overcome these sources of uncertainty, it must be able to learn from available data on its own, and others' interactions with the world. In many settings, this data is scarce and expensive to acquire. Moreover, this data is often incomplete - providing only a partial description of the state of the world and how it evolves. If machines are to be able to predict the outcomes of their actions, they must build models of their worlds from limited and incomplete data. In some settings, we may use experts to show machines how to act optimally - using them to correct the mistakes machines make. It is of paramount importance that we can guarantee the safety of the frameworks in which we allow the machines and experts to interact. The work in this thesis addresses the challenges of learning components of decision-systems from data. In the first part of this thesis, we present Structured Mechanical Models, a flexible model class that can learn the dynamics of physical systems from limited data. We then turn to the problem of partially observed systems, for which the data available does not reveal their full state. We present an algorithm called Certainty-Equivalent Expectation-Maximization, which can efficiently learn the dynamics of nonlinear, high-dimensional, and partially observed systems. We demonstrate the performance of this algorithm on multiple challenging domains such as an aerobatic helicopter, and apply it to the task of learning models of the spread of COVID-19. Finally, we study the problem of safely allowing an expert to correct the actions of a learned decision system to teach it optimal behavior. We propose an algorithm called EnsembleDAgger, which trains a Bayesian decision system on data from the expert, and uses the system's uncertainty to safely and effectively allow it to interact with an expert.

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 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.

Download or read book Computer Vision ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-10-22 with total page 812 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Proceedings of 2021 International Conference on Autonomous Unmanned Systems ICAUS 2021 written by Meiping Wu and published by Springer Nature. This book was released on 2022-03-18 with total page 3575 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original, peer-reviewed research papers from the ICAUS 2021, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2021 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.

Download or read book Handbook of Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2023-10-16 with total page 937 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).

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 Nonlinear Structures Systems Volume 1 written by Matthew R.W. Brake and published by Springer Nature. This book was released on 2023-11-14 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Structures & Systems, Volume 1: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the first volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Nonlinear Dynamics, including papers on: Experimental Nonlinear Dynamics Jointed Structures: Identification, Mechanics, Dynamics Nonlinear Damping Nonlinear Modeling and Simulation Nonlinear Reduced-Order Modeling Nonlinearity and System Identification

Download or read book Applied Intelligence for Industry 4 0 written by Nazmul Siddique and published by CRC Press. This book was released on 2023-06-12 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores text mining and IoT applications for monitoring and controlling smart industrial systems Describes the key principles and techniques for Big-data analytics, security, and optimization for industrial applications. Provides context-aware insights, human-centric industry, smart computing for next-generation industry

Download or read book Data driven Modeling with Hybrid Dynamical Systems written by Bora S. Banjanin and published by . This book was released on 2019 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hybrid dynamical systems are used to describe systems that can instantaneously change state and dynamics. At small timescales, continuous electrodynamics govern the interaction of rigid bodies. Simulating the corresponding stiff differential equation introduces unnecessary complexity when the restitution of velocities post-impact is the phenomenon of interest. Although classical mathematics and physics deals primarily with smooth physical processes, the dynamics of real-world systems can and does abruptly change. We can learn from data to inform the structure and fit the parameters of hybrid dynamical models for such systems. These data-driven methods leverage developments in sensing and computation and are a natural progression in the study of modeling and controlling systems. Continuously collecting data can yield interactive systems that adapt towards a target behavior. An accurate computational model can also verify the safety and efficacy of engineered systems. This thesis seeks to further the practical application of data-driven hybrid dynamical systems - to control robotic systems and assistive devices. In the first aim, hybrid dynamical systems are commonly used to model mechanical systems subject to unilateral constraints, e.g. legged locomotion. We demonstrated that nonsmoothness can cause standard optimization techniques to lose convergence guarantees and contribute to poor performance for the resulting control policy. The second aim seeks to predict rhythmic human locomotion with a motive to improve the clinical prescription of Ankle Foot Orthoses (AFO). We created subject-specific models that can predict how an individual will respond to an untested AFO torque profile. These aims tie together advancements in data science with the inherent ability of hybrid dynamical systems to represent phenomena of interest in the real world.

Download or read book Making Sense of a Complex World A Data Driven Approach written by Thomas N. Thiem and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation we develop a suite of data-driven modeling techniques for dynamical systems by leveraging manifold learning, dimensionality reduction, and deep learning methodologies. We apply these techniques to a variety of systems including coupled oscillators, agent-based systems, and optimization and root-finding algorithms. With each system we demonstrate a different facet of the techniques.Beginning with coupled oscillator systems in chapter 1, we describe how manifold learning can be used to tackle the challenge of finding coarse descriptions of collective dynamics. We then present a specialized neural network architecture that allows us to learn the dynamics of coarse variables directly from time series. Finally, we leverage manifold learning once again to find reduced sets of parameters, the "effective" parameters, for multi-parameter coupled oscillator models.Moving on to heterogeneous agent-based models in chapter 2, we present techniques for deriving data-driven surrogate models in the presence of low-dimensional dynamics. We present a global approach, that constructs a basis from spatiotemporal data and learns a system of ODEs in the basis coordinates, and a local approach, that learns a PDE in heterogeneity space. We discuss the implications of an agent-based PDE and illustrate a subsampling approach. In chapter 3 we utilize manifold learning to extend our agent-based PDE approach to cases in which the heterogeneities are unknown. We present examples of this modification for both one and two-dimensional data-driven spaces.We conclude by examining algorithms from the Koopman point of view in chapter 4. We discuss how employing the Koopman framework proves beneficial for the analysis and acceleration of algorithms and showcase the approach with examples including both the gradient descent and Nesterov optimization algorithms and the Newton-Raphson root-finding algorithm.As the world continues to become ever more saturated in data new analysis techniques will be required to extract meaningful information. We believe that our data-driven modeling techniques offer a step toward making sense of a data-driven future.