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Advances In Data Driven Approaches And Modeling Of Complex Systems
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Book Synopsis Advances in data-driven approaches and modeling of complex systems by : Mohd Hafiz Mohd
Download or read book Advances in data-driven approaches and modeling of complex systems written by Mohd Hafiz Mohd and published by Frontiers Media SA. This book was released on 2023-06-27 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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.
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 Data-driven Approaches for Complex Systems by : Connor Anthony Verheyen
Download or read book Data-driven Approaches for Complex Systems written by Connor Anthony Verheyen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many research efforts to advance human health and well-being involve interdisciplinary problem spaces and complex, poorly-understood systems. This thesis integrates both computational and experimental approaches to advance our understanding and control of complex systems at the interface of machine learning, materials science, and manufacturing. Specifically, I demonstrate the data-driven description of supervised machine learning for biomedical engineering tasks, the data-driven design of optimized soft granular biomaterials, and the proof-of-concept development of a transcatheter additive manufacturing platform. In Part 1, I develop custom software for high-resolution, multifactorial machine learning (ML) experiments. I iteratively apply this workflow to a set of diverse ML problems from the biomedical engineering (BME) domain to generate massive meta-datasets covering each phase of the hierarchical ML optimization and evaluation process. Then, I describe the underlying patterns and heterogeneity in these rich datasets and delineate empirical guidelines for the rigorous and reliable adoption of machine learning for BME problems. In Part 2, I leverage the insights from Part 1 to develop a flexible and robust data-driven modeling pipeline for complex soft materials. The pipeline can be applied after each round of experimentation to build predictive models, extract key design rules, and generate data-driven design frameworks. I use this integrated, stepwise approach to optimize the structures, properties, and performance profiles of soft granular biomaterials for injection- and extrusion-based biomedical applications. In Part 3, I leverage the optimized materials from Part 2 to develop a novel microgel-based transcatheter additive manufacturing technology. I obtain proof-of-concept data for the platform's critical features, including controlled transcatheter material delivery to distant target locations, rapid in situ structuration of arbitrary 3D constructs, and reliable scaffold stabilization to ensure long-term implant integrity. Together, this work paves the way for minimally-invasive, patient-specific, in situ biofabrication.
Book Synopsis Social-Behavioral Modeling for Complex Systems by : Paul K. Davis
Download or read book Social-Behavioral Modeling for Complex Systems written by Paul K. Davis and published by John Wiley & Sons. This book was released on 2019-03-13 with total page 992 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming. Recent scientific and technological advances have created exciting opportunities for such improvements. However, the book also identifies crucial scientific, ethical, and cultural challenges to be met if social-behavioral modeling is to achieve its potential. Doing so will require new methods, data sources, and technology. The volume discusses these, including those needed to achieve and maintain high standards of ethics and privacy. The result should be a new generation of modeling that will advance science and, separately, aid decision-making on major social and security-related subjects despite the myriad uncertainties and complexities of social phenomena. Intended to be relatively comprehensive in scope, the volume balances theory-driven, data-driven, and hybrid approaches. The latter may be rapidly iterative, as when artificial-intelligence methods are coupled with theory-driven insights to build models that are sound, comprehensible and usable in new situations. With the intent of being a milestone document that sketches a research agenda for the next decade, the volume draws on the wisdom, ideas and suggestions of many noted researchers who draw in turn from anthropology, communications, complexity science, computer science, defense planning, economics, engineering, health systems, medicine, neuroscience, physics, political science, psychology, public policy and sociology. In brief, the volume discusses: Cutting-edge challenges and opportunities in modeling for social and behavioral science Special requirements for achieving high standards of privacy and ethics New approaches for developing theory while exploiting both empirical and computational data Issues of reproducibility, communication, explanation, and validation Special requirements for models intended to inform decision making about complex social systems
Book Synopsis Data-Driven Modeling & Scientific Computation by : J. Nathan Kutz
Download or read book Data-Driven Modeling & Scientific Computation written by J. Nathan Kutz and published by OUP Oxford. This book was released on 2013-08-08 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: · statistics, · time-frequency analysis, and · low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.
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 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.
Book Synopsis Advances In Data-based Approaches For Hydrologic Modeling And Forecasting by : Bellie Sivakumar
Download or read book Advances In Data-based Approaches For Hydrologic Modeling And Forecasting written by Bellie Sivakumar and published by World Scientific. This book was released on 2010-08-10 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Book Synopsis Data Engineering and Data Science by : Kukatlapalli Pradeep Kumar
Download or read book Data Engineering and Data Science written by Kukatlapalli Pradeep Kumar and published by John Wiley & Sons. This book was released on 2023-08-29 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book 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. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
Book Synopsis Data-driven Diagnostics and Prognostics for Complex Systems by : Junchuan Shi
Download or read book Data-driven Diagnostics and Prognostics for Complex Systems written by Junchuan Shi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in artificial intelligence or machine learning have the potential to significantly improve the effectiveness and efficiency of diagnostic and prognostic techniques. The objective of this research is to develop novel data-driven predictive models with machine learning and deep learning algorithms that allow one to model the degradation, detect the faults, as well as predict the remaining useful life (RUL) of complex systems, including bearings, gearboxes, and Lithium-ion (Li-ion) batteries. First, an enhanced ensemble learning algorithm is developed to improve the accuracy of RUL prediction by selecting diverse base learners and features at different degradation stages. The proposed method with increased diversity in base learners and features was demonstrated to be more accurate than other reported algorithms. Second, a convolutional long short-term memory (Conv-LSTM) approach is introduced to accurately classify the type, position, and direction of gear faults under different operating conditions by extracting spatiotemporal features from multiple sensors. The proposed method achieved 95% classification accuracy of fault type and 80% classification accuracy of fault location. Third, a deep learning method that combines convolutional neural networks (CNN) and bi-directional long short-term memory (BiLSTM) is developed to predict the discharge capacity and the end-of-discharge (EOD) of Li-ion batteries. The results show that by considering the discharge capacity estimated by CNN, the MAPE of EOD prediction using BiLSTM decreased from 8.52% to 3.21%. Fourth, a physics-informed machine learning method that combines the calendar and cycle aging (CCA) model and a LSTM model is developed to predict battery degradation behavior and RUL under different working conditions. The results show that the proposed method can predict the RUL of batteries accurately (10% in term of MAPE).
Book Synopsis Big Data in Complex Systems by : Aboul Ella Hassanien
Download or read book Big Data in Complex Systems written by Aboul Ella Hassanien and published by Springer. This book was released on 2015-01-02 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.
Book Synopsis Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems by : M. Reza Rahimi Tabar
Download or read book Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems written by M. Reza Rahimi Tabar and published by Springer. This book was released on 2019-07-04 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation? Here, the term "non-parametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data. The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures. Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results. The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is self-contained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations. The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements.
Book Synopsis Dynamic Data Driven Applications Systems by : Frederica Darema
Download or read book Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2020-11-02 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Dynamic Data Driven Application Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020. The 21 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They cover topics such as: digital twins; environment cognizant adaptive-planning systems; energy systems; materials systems; physics-based systems analysis; imaging methods and systems; and learning systems.
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309494206 Total Pages :79 pages Book Rating :4.3/5 (94 download)
Book Synopsis Data-Driven Modeling for Additive Manufacturing of Metals by : National Academies of Sciences, Engineering, and Medicine
Download or read book Data-Driven Modeling for Additive Manufacturing of Metals written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-11-09 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.
Book Synopsis Data-driven Model Order Reduction by : Zhan Ma (Ph.D.)
Download or read book Data-driven Model Order Reduction written by Zhan Ma (Ph.D.) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of machine learning (ML) techniques, data-driven approaches has become increasingly popular when constructing the reduced-order models (ROMs) for complex systems. Since data-driven model order reduction (MOR) does not require the information of underlying governing equations, it is suitable for many ``black-box" problems. These models also hold the potential to significantly reduce the computational cost of high-fidelity numerical simulations. However, due to the lack of physical interpretability, purely data-driven ROMs often show poor performance on generalization, and even worse, lead to predictions that are physically implausible and inconsistent, even they fit the data very well on training set. This thesis aims to construct data-driven ROMs for different physical systems by combining advanced machine learning models and techniques with physical domain knowledge. To be more specific, this thesis focuses on constructing data-driven ROMs for three physical systems: (1) dynamical systems with moving boundaries, (2) polymer systems, and (3) particulate suspension problems. These proposed MOR frameworks are customized to address the specific challenges presented by each physical system, integrating the most suitable machine learning techniques. Moreover, we will also propose innovative machine learning architectures to better suit our specific problems. For example, we design a novel graph neural network (GNN) architecture for modeling particulate suspension. This thesis is organized as follows. Chapter 1 introduces the overall background and motivation of data-driven model order reduction, as well as the outline of the whole thesis. Chapter 2 focuses on the dynamical systems with moving boundaries and builds a data-driven ROM to predict the full-order solution in future time, which can effectively reduce the cost of high-fidelity simulations or experiments. Chapter 3 presents a data-driven coarse-grained (CG) modeling framework, which eliminates the unimportant or irrelevant degrees of freedoms (DOFs) and preserves the dynamic properties of interested variables. Chapter 4 proposes a novel graph neural network framework to efficiently simulate particle suspensions in Stokes flow. Here, the DOFs of fluid are removed by learning the hydrodynamic interactions in the proposed GNN framework. Chapter 5 summarizes the thesis and highlights our contributions. In summary, by integrating the ML techniques with domain-specific knowledge, this thesis develops data-driven reduced-order models for three physical systems. Through theoretical analysis and numerical examples, the established ROMs facilitate accurate and efficient predictions of the interested variables, showcasing significant potential for applications in real-life engineering scenarios.
Book Synopsis Data-driven Modeling, Analysis, and Optimization of Sensor-integrated Complex Systems by : Rui Zhu
Download or read book Data-driven Modeling, Analysis, and Optimization of Sensor-integrated Complex Systems written by Rui Zhu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced sensing is increasingly integrated with complex systems for system informatics and optimization. Rapid advancement of sensing technology brings the data proliferation and provides unprecedented opportunities for data-driven modeling, analysis, and optimization of sensor-integrated complex systems. However, complex-structured sensing data pose significant challenges in data analysis. Realizing full potentials of sensing data depends to a great extent on developing novel analytical methods and tools to address the challenges. The objective of this dissertation is to develop innovative sensor-based methodologies for modeling, analysis, and optimization of complex healthcare and virtual reality (VR) systems. This research will enable and assist in 1) handling high-dimensional spatiotemporal data; 2) extracting pertinent information about system dynamics; 3) exploiting acquired knowledge for system optimization for the cardiovascular system and the human behavior in VR environment. My research accomplishments include: Optimal sensing strategy for the design of electrocardiogram imaging (ECGi) system: In Chapter 2, a new optimal sensor placement strategy is developed for the design of ECGi systems to capture a complete picture of spatiotemporal dynamics in cardiac electrical activity. This investigation provides a viable solution that uses a sparse set of ECG sensors to realize high-resolution ECGi systems. Sensor-based survival analysis of cardiac risks: In Chapter 3, a data-driven survival model is developed to predict the probability that cardiac events occur at a certain time point by integrating variable data, attribute data, with sensor-based ECG data. This research is conducive to improve the early detection of life-threatening cardiac events, thereby reducing the recurrences of cardiac events and improving lifestyle modifications of cardiac patients. Joint SDT-C&E model for quantifying problem-solving skills in sensor-based VR: In Chapter 4, a data-driven model that integrates signal detection theory (SDT) with conflict & error (C&E) is developed to quantify engineering problem-solving skills. The proposed model can be generalized to quantify problem-solving skills in many other disciplines such as healthcare, psychology, and cognitive sciences, by comparing one's problem-solving actions with actions of a subject matter expert. Eye-tracking sensing and modeling in VR: In Chapter 5, a VR learning factory is developed to mimic physical learning factories. Further, data-driven models are integrated with eye-tracking sensing to evaluate and reinforce problem-solving skills of engineering students in a VR learning factory. The VR learning factory and aggregative quantifier developed in this chapter have strong potentials to be incorporated into laboratory demonstration and engineering examinations of manufacturing curriculums.
