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Author: Mohd Hafiz Mohd
Publisher: Frontiers Media SA
ISBN: 2832526659
Category : Science
Languages : en
Pages : 133
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Book Description
Author: Mohd Hafiz Mohd
Publisher: Frontiers Media SA
ISBN: 2832526659
Category : Science
Languages : en
Pages : 133
Get Book Here
Book Description
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
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Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author: J. Nathan Kutz
Publisher: SIAM
ISBN: 1611974496
Category : Science
Languages : en
Pages : 241
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Book Description
Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.
Author: Jose Nathan Kutz
Publisher:
ISBN: 0199660336
Category : Computers
Languages : en
Pages : 657
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Book Description
Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.
Author: Connor Anthony Verheyen
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Paul K. Davis
Publisher: John Wiley & Sons
ISBN: 1119484987
Category : Technology & Engineering
Languages : en
Pages : 992
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Book Description
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
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009115634
Category : Computers
Languages : en
Pages : 616
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Book Description
Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Author: Kukatlapalli Pradeep Kumar
Publisher: John Wiley & Sons
ISBN: 1119841976
Category : Mathematics
Languages : en
Pages : 367
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Book Description
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.
Author: M. Reza Rahimi Tabar
Publisher: Springer
ISBN: 3030184722
Category : Science
Languages : en
Pages : 280
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Book Description
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.
Author: Junchuan Shi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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).
