| data driven engineering book: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| data driven engineering book: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 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. |
| data driven engineering book: Data-Driven Engineering Design Ang Liu, Yuchen Wang, Xingzhi Wang, 2021-10-09 This book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design. Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation. Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design. |
| data driven engineering book: Data-Driven Technology for Engineering Systems Health Management Gang Niu, 2016-07-27 This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis. |
| data driven engineering book: Data-Driven Modeling & Scientific Computation Jose Nathan Kutz, 2013-08-08 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. |
| data driven engineering book: Machine Learning Control – Taming Nonlinear Dynamics and Turbulence Thomas Duriez, Steven L. Brunton, Bernd R. Noack, 2016-11-02 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. |
| data driven engineering book: Dynamic Mode Decomposition J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor, 2016-11-23 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. |
| data driven engineering book: Data-Driven Controller Design Alexandre Sanfelice Bazanella, Lucíola Campestrini, Diego Eckhard, 2011-11-16 Data-Based Controller Design presents a comprehensive analysis of data-based control design. It brings together the different data-based design methods that have been presented in the literature since the late 1990’s. To the best knowledge of the author, these data-based design methods have never been collected in a single text, analyzed in depth or compared to each other, and this severely limits their widespread application. In this book these methods will be presented under a common theoretical framework, which fits also a large family of adaptive control methods: the MRAC (Model Reference Adaptive Control) methods. This common theoretical framework has been developed and presented very recently. The book is primarily intended for PhD students and researchers - senior or junior - in control systems. It should serve as teaching material for data-based and adaptive control courses at the graduate level, as well as for reference material for PhD theses. It should also be useful for advanced engineers willing to apply data-based design. As a matter of fact, the concepts in this book are being used, under the author’s supervision, for developing new software products in a automation company. The book will present simulation examples along the text. Practical applications of the concepts and methodologies will be presented in a specific chapter. |
| data driven engineering book: Data-Driven Traffic Engineering Hubert Rehborn, Micha Koller, Stefan Kaufmann, 2020-11-08 Data-Driven Traffic Engineering: Understanding of Traffic and Applications Based on Three-Phase Traffic Theory shifts the current focus from using modeling and simulation data for traffic measurements to the use of actual data. The book uses real-world, empirically-derived data from a large fleet of connected vehicles, local observations and aerial observation to shed light on key traffic phenomena. Readers will learn how to develop an understanding of the empirical features of vehicular traffic networks and how to consider these features in emerging, intelligent transport systems. Topics cover congestion patterns, fuel consumption, the influence of weather, and much more. This book offers a unique, data-driven analysis of vehicular traffic in traffic networks, also considering how to apply data-driven insights to the intelligent transport systems of the future. Provides an empirically-driven analysis of traffic measurements/congestion based on real-world data collected from a global fleet of vehicles Applies Kerner's three-phase traffic theory to empirical data Offers a critical scientific understanding of the underlying concerns of traffic control in automated driving and intelligent transport systems |
| data driven engineering book: Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering Shahab Araghinejad, 2013-11-26 “Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering. |
| data driven engineering book: Creating a Data-Driven Organization Carl Anderson, 2015-07-23 What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models--Publisher's description. |
| data driven engineering book: Data Driven: Harnessing Data and AI to Reinvent Customer Engagement Tom Chavez, Chris O’Hara, Vivek Vaidya, 2018-10-05 Axiom Business Book Award Silver Medalist in Business TechnologyThe indispensable guide to data-powered marketing from the team behind the data management platform that helps fuel Salesforce―the #1 customer relationship management (CRM) company in the worldA tectonic shift in the practice of marketing is underway. Digital technology, social media, and e-commerce have radically changed the way consumers access information, order products, and shop for services. Using the latest technologies―cloud, mobile, social, internet of things (IoT), and artificial intelligence (AI)―we have more data about consumers and their needs, wants, and affinities than ever before. Data Driven will show you how to:●Target and delight your customers with unprecedented accuracy and success●Bring customers closer to your brand and inspire them to engage, purchase, and remain loyal●Capture, organize, and analyze data from every source and activate it across every channel●Create a data-powered marketing strategy that can be customized for any audience●Serve individual consumers with highly personalized interactions●Deliver better customer service for the best customer experience●Improve your products and optimize your operating systems●Use AI and IoT to predict the future direction of marketsYou’ll discover the three principles for building a successful data strategy and the five sources of data-driven power. You’ll see how top companies put these data-driven strategies into action: how Pandora used second- and third-hand data to learn more about its listeners; how Georgia-Pacific moved from scarcity to abundance in the data sphere; and how Dunkin’ Brands leveraged CRM data as a force multiplier for customer engagement. And if you’re wondering what the future holds, you’ll receive seven forecasts to better prepare you for what may come next. Sure to be a classic, Data Driven is a practical road map to the modern marketing landscape and a toolkit for success in the face of changes already underway and still to come. |
| data driven engineering book: Advances and Applications in Model-Driven Engineering Díaz, Vicente García, 2013-08-31 As organizations and research institutions continue to emphasize model-driven engineering (MDE) as a first-class approach in the software development process of complex systems, the utilization of software in multiple domains and professional networks is becoming increasingly vital. Advances and Applications in Model-Driven Engineering explores this relatively new approach in software development that can increase the level of abstraction of development of tasks. This publication covers the issues of bridging the gaps between various disciplines within software engineering and computer science. Professionals, researchers, and students will discover the most current tools and techniques available in the field to maximize efficiency of model-driven software development. |
| data driven engineering book: Data-Driven Computational Methods John Harlim, 2018-07-12 Describes computational methods for parametric and nonparametric modeling of stochastic dynamics. Aimed at graduate students, and suitable for self-study. |
| data driven engineering book: Data-Driven Personas Bernard J. Jansen, Joni Salminen, 2022-05-31 Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understanding functionalities for anyone needing such insights. |
| data driven engineering book: Data-Driven Evolutionary Modeling in Materials Technology Nirupam Chakraborti, 2022-09-15 Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials. |
| data driven engineering book: Data Driven Smart Manufacturing Technologies and Applications Weidong Li, Yuchen Liang, Sheng Wang, 2021-02-20 This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0. |
| data driven engineering book: Data-Driven and Model-Based Methods for Fault Detection and Diagnosis Majdi Mansouri, Mohamed-Faouzi Harkat, Hazem N. Nounou, Mohamed N. Nounou, 2020-02-05 Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. - Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) - Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection - Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection - Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches - Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data |
| data driven engineering book: Data-Driven Storytelling Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, Sheelagh Carpendale, 2018-03-28 This book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners. |
| data driven engineering book: Designing Data-Intensive Applications Martin Kleppmann, 2017-03-16 Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures |
| data driven engineering book: Data-Driven Modeling for Sustainable Engineering Kondo H. Adjallah, Babiga Birregah, Henry Fonbeyin Abanda, 2019-06-21 This book gathers the proceedings of the 1st International Conference on Engineering, Applied Sciences and System Modeling (ICEASSM), a four-day event (18th–21st April 2017) held in Accra, Ghana. It focuses on research work promoting a better understanding of engineering problems through applied sciences and modeling, and on solutions generated in an African setting but with relevance to the world as a whole. The book provides a holistic overview of challenges facing Africa, and addresses various areas from research and development perspectives. Presenting contributions by scientists, engineers and experts hailing from a host of international institutions, the book offers original approaches and technological solutions to help solve real-world problems through research and knowledge sharing. Further, it explores promising opportunities for collaborative research on issues of scientific, economic and social development, making it of interest to researchers, scientists and practitioners looking to conduct research in disciplines such as water supply, control, civil engineering, statistical modeling, renewable energy and sustainable urban development. |
| data driven engineering book: Data-driven Reservoir Modeling Shahab D. Mohaghegh, 2017 |
| data driven engineering book: Data-Driven Remaining Useful Life Prognosis Techniques Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu, 2017-01-20 This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making. |
| data driven engineering book: Driving Eureka! Doug Hall, 2018-11-13 Transform the art of innovation into a reliable system! System Driven Innovation enables you and everyone on your team to use innovation to work smarter, faster, and more creatively. It transforms innovation from a random act to a reliable science. This new mindset ignites confidence in the future. It enables the creation of bigger and bolder ideas—and turns them into reality faster, smarter, and more successfully. With this new mindset, innovation by everyone, everywhere, every day becomes the norm. The rapidly changing world becomes a tremendous opportunity to achieve greatness. Innovation Engineering defines innovation in two words: Meaningfully Unique. When a product, service, or job candidate is Meaningfully Unique customers are willing to pay more money for it. This links to the two simple truths in today’s marketplace: If you’re Meaningfully Unique life is great! If you’re NOT Meaningfully Unique you’d better be cheap. Innovation Engineering is a new field of academic study and leadership science. It teaches how to apply the science of system thinking to strategy, innovation, and cooperation. Research finds that it helps to increase innovation speed (up to 6x) and decrease risk (by 30 to 80%). Innovation Engineering accelerates the creation and development of more profitable products and services. However, the bigger benefit may well lie in its ability to transform organizational cultures by enabling everyone to work smarter every day. What makes Innovation Engineering unique is that it’s grounded in data, backed by academic theory, and validated in real-world practice. Collectively, it’s the number one documented innovation system on earth. Over 35,000 people have been educated in Innovation Engineering classes, and more than $15 billion in innovations are in active development. In his book Driving Eureka!, best-selling business author Doug Hall presents the System Driven Innovation scientific method for enabling innovation by everyone, everywhere, every day. It’s the essential resource you need to enable yourself—and your team—to innovate, succeed, and do amazing things that matter, on a daily basis. |
| data driven engineering book: Data-Oriented Design Richard Fabian, 2018-09-29 The projects tackled by the software development industry have grown in scale and complexity. Costs are increasing along with the number of developers. Power bills for distributed projects have reached the point where optimisations pay literal dividends. Over the last 10 years, a software development movement has gained traction, a movement founded in games development. The limited resources and complexity of the software and hardware needed to ship modern game titles demanded a different approach. Data-oriented design is inspired by high-performance computing techniques, database design, and functional programming values. It provides a practical methodology that reduces complexity while improving performance of both your development team and your product. Understand the goal, understand the data, understand the hardware, develop the solution. This book presents foundations and principles helping to build a deeper understanding of data-oriented design. It provides instruction on the thought processes involved when considering data as the primary detail of any project. |
| data driven engineering book: Model-Driven Software Engineering in Practice Marco Brambilla, Jordi Cabot, Manuel Wimmer, 2017-03-30 This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, application scenarios, and current standards, like the well-known MDA initiative proposed by OMG (Object Management Group) as well as the practices on how to integrate MDSE in existing development processes. The second part deals with the technical aspects of MDSE, spanning from the basics on when and how to build a domain-specific modeling language, to the description of Model-to-Text and Model-to-Model transformations, and the tools that support the management of MDSE projects. The second edition of the book features: a set of completely new topics, including: full example of the creation of a new modeling language (IFML), discussion of modeling issues and approaches in specific domains, like business process modeling, user interaction modeling, and enterprise architecture complete revision of examples, figures, and text, for improving readability, understandability, and coherence better formulation of definitions, dependencies between concepts and ideas addition of a complete index of book content In addition to the contents of the book, more resources are provided on the book's website http://www.mdse-book.com, including the examples presented in the book. |
| data driven engineering book: Data-Driven Marketing Mark Jeffery, 2010-02-08 NAMED BEST MARKETING BOOK OF 2011 BY THE AMERICAN MARKETING ASSOCIATION How organizations can deliver significant performance gains through strategic investment in marketing In the new era of tight marketing budgets, no organization can continue to spend on marketing without knowing what's working and what's wasted. Data-driven marketing improves efficiency and effectiveness of marketing expenditures across the spectrum of marketing activities from branding and awareness, trail and loyalty, to new product launch and Internet marketing. Based on new research from the Kellogg School of Management, this book is a clear and convincing guide to using a more rigorous, data-driven strategic approach to deliver significant performance gains from your marketing. Explains how to use data-driven marketing to deliver return on marketing investment (ROMI) in any organization In-depth discussion of the fifteen key metrics every marketer should know Based on original research from America's leading marketing business school, complemented by experience teaching ROMI to executives at Microsoft, DuPont, Nisan, Philips, Sony and many other firms Uses data from a rigorous survey on strategic marketing performance management of 252 Fortune 1000 firms, capturing $53 billion of annual marketing spending In-depth examples of how to apply the principles in small and large organizations Free downloadable ROMI templates for all examples given in the book With every department under the microscope looking for results, those who properly use data to optimize their marketing are going to come out on top every time. |
| data driven engineering book: Informatics for Materials Science and Engineering: Data-Driven Discovery for Accelerated Experimentation and Application Krishna Rajan, 2017-11-13 Materials informatics: a hot topic area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis. The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this quantitative avalanche and the resulting complex, multi-factor analyses required to understand it means that interest, investment, and research are revisiting informatics approaches as a solution. This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science. This solutions-based reference synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field. Identifies and analyzes interdisciplinary strategies (including combinatorial and high throughput approaches) that accelerate materials development cycle times and reduces associated costs Mathematical and computational analysis aids formulation of new structure-property correlations among large, heterogeneous, and distributed data sets Practical examples, computational tools, and software analysis benefits rapid identification of critical data and analysis of theoretical needs for future problems |
| data driven engineering book: Model-Driven Engineering and Software Development Slimane Hammoudi, Luís Ferreira Pires, Bran Selić, 2021-02-01 This book constitutes thoroughly revised and selected papers from the 8th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2020, held in Valletta, Malta, in February 2020. The 15 revised and extended papers presented in this volume were carefully reviewed and selected from 66 submissions. They present recent research results and development activities in using models and model driven engineering techniques for software development. The papers are organized in topical sections on methodologies, processes and platforms; applications and software development; modeling languages, tools and architectures. |
| data driven engineering book: Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes Evan L. Russell, Leo H. Chiang, Richard D. Braatz, 2012-12-06 Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis. The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application. |
| data driven engineering book: Computational Epidemiology Ellen Kuhl, 2021-09-22 This innovative textbook brings together modern concepts in mathematical epidemiology, computational modeling, physics-based simulation, data science, and machine learning to understand one of the most significant problems of our current time, the outbreak dynamics and outbreak control of COVID-19. It teaches the relevant tools to model and simulate nonlinear dynamic systems in view of a global pandemic that is acutely relevant to human health. If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it. |
| data driven engineering book: Computational and Data-Driven Chemistry Using Artificial Intelligence Takashiro Akitsu, 2021-10-08 Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Drawing on the knowledge of its expert team of global contributors, the book offers fascinating insight into this rapidly developing field and serves as a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. - Provides an accessible introduction to the current state and future possibilities for AI in chemistry - Explores how computational chemistry methods and approaches can both enhance and be enhanced by AI - Highlights the interdisciplinary and broad applicability of AI tools across a wide range of chemistry fields |
| data driven engineering book: Data-Driven Optimization of Manufacturing Processes Kalita, Kanak, Ghadai, Ranjan Kumar, Gao, Xiao-Zhi, 2020-12-25 All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes. |
| data driven engineering book: Designing with Data Rochelle King, Elizabeth F Churchill, Caitlin Tan, 2017-03-29 On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data. This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow. Understand the relationship between data, business, and design Get a firm grounding in data, data types, and components of A/B testing Use an experimentation framework to define opportunities, formulate hypotheses, and test different options Create hypotheses that connect to key metrics and business goals Design proposed solutions for hypotheses that are most promising Interpret the results of an A/B test and determine your next move |
| data driven engineering book: Shale Analytics Shahab D. Mohaghegh, 2017-02-09 This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations. |
| data driven engineering book: Protein Engineering Huimin Zhao, 2021-08-23 A one-stop reference that reviews protein design strategies to applications in industrial and medical biotechnology Protein Engineering: Tools and Applications is a comprehensive resource that offers a systematic and comprehensive review of the most recent advances in the field, and contains detailed information on the methodologies and strategies behind these approaches. The authors—noted experts on the topic—explore the distinctive advantages and disadvantages of the presented methodologies and strategies in a targeted and focused manner that allows for the adaptation and implementation of the strategies for new applications. The book contains information on the directed evolution, rational design, and semi-rational design of proteins and offers a review of the most recent applications in industrial and medical biotechnology. This important book: Covers technologies and methodologies used in protein engineering Includes the strategies behind the approaches, designed to help with the adaptation and implementation of these strategies for new applications Offers a comprehensive and thorough treatment of protein engineering from primary strategies to applications in industrial and medical biotechnology Presents cutting edge advances in the continuously evolving field of protein engineering Written for students and professionals of bioengineering, biotechnology, biochemistry, Protein Engineering: Tools and Applications offers an essential resource to the design strategies in protein engineering and reviews recent applications. |
| data driven engineering book: Model-Driven Engineering and Software Development Slimane Hammoudi, Luís Ferreira Pires, Bran Selić, 2020-01-03 This book constitutes thoroughly revised and selected papers from the 7th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2019, held in Prague, Czech Republic, in February 2019. The 16 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 76 submissions. They address some of the most relevant challenges being faced by researchers and practitioners in the field of model-driven engineering and software development and cover topics like language design and tooling; programming support tools; code and text generation from models, behavior modeling and analysis; model transformations and multi-view modeling; as well as applications of MDD and its related techniques to cyber-physical systems, cyber security, IoT, autonomous vehicles and healthcare. |
| data driven engineering book: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data driven engineering book: Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems Steven X. Ding, 2014-04-12 Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and demonstrated on industrial case systems. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems will be of interest to process and control engineers, engineering students and researchers with a control engineering background. |
| data driven engineering book: Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems M. Reza Rahimi Tabar, 2019-07-04 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. |
Data-Driven Science and Engineering : Machine Learning, …
“Brunton and Kutz’s book is fast becoming an indispensable resource for machine learn-ing and data-driven learning in science and engineering. The second edition adds several timely topics …
Fundamentals of Data Engineering - cdn.bookey.app
By exploring essential concepts such as data generation, ingestion, orchestration, transformation, storage, governance, and deployment, this book equips you with the tools to tackle data …
Ang Liu Yuchen Wang Xingzhi Wang Data-Driven Engineering …
mplete data lifecycle is divided into multiple data operations. A theoretical framework of data-driven engineering design is presented to couple various design operations with relevan.
Data-Driven Engineering Design - Springer
data science puts forward an appealing possibility for engineering design, i.e., how, in what ways, and to what extent can data be directly employed to drive design, especially in the context of …
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Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for modelling and control in engineering …
Data Driven Science And Engineering - obiemaps.oberlin.edu
Now with MATLAB, Data-Driven Science and Engineering trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the …
Data-DrivenOptimization - Stanford University
We want to choose an intermediate approach between stochastic optimization, which has no robustness to the error of distribution; and robust optimization, which ignores available …
Data-Driven Science and Engineering - Cambridge University …
“Brunton and Kutz’s book is fast becoming an indispensable resource for machine learn-ing and data-driven learning in science and engineering. The second edition adds several timely topics …
Fundamentals Of Data Engineering (Download Only)
Importance of Data Engineering in Modern Businesses In today's data-driven world, data engineering is not just a mere cog in the machine; it is the driving force behind innovation, …
An Engineering Data - vols.wta.org
paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to …
Data-Driven Science and Engineering
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical …
Data-Driven Science and Engineering - Cambridge University …
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical …
Data Driven Science And Engineering (book)
Data Driven Science And Engineering : Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning …
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creating a dynamic, data-driven replica of infrastructure systems. These tools allow operators to simulate performance scenarios, test maintenance strategies over the full asset lifecycle.
Data Driven Science And Engineering (book)
Data Driven Science And Engineering : Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning …
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With this practical book, you’ll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the …
Introduction to Computational
Introduction to Computational Engineering with MATLAB® aims to teach readers how to use MATLAB® programming to solve numerical engineering problems. The book focuses on …
Data Driven Science And Engineering (book)
Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for modelling and control in engineering …
Data Driven Science And Engineering Copy
Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for modelling and control in engineering …
Data Driven Science And Engineering (2024)
Data Driven Science And Engineering: Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for …
Data-Driven Science and Engineering : Machine Learning, …
“Brunton and Kutz’s book is fast becoming an indispensable resource for machine learn-ing and data-driven learning in science and engineering. The second edition adds several timely topics …
Fundamentals of Data Engineering - cdn.bookey.app
By exploring essential concepts such as data generation, ingestion, orchestration, transformation, storage, governance, and deployment, this book equips you with the tools to tackle data …
Ang Liu Yuchen Wang Xingzhi Wang Data-Driven …
mplete data lifecycle is divided into multiple data operations. A theoretical framework of data-driven engineering design is presented to couple various design operations with relevan.
Data-Driven Engineering Design - Springer
data science puts forward an appealing possibility for engineering design, i.e., how, in what ways, and to what extent can data be directly employed to drive design, especially in the context of …
Data Driven Science And Engineering Copy
Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for modelling and control in engineering …
Data Driven Science And Engineering - obiemaps.oberlin.edu
Now with MATLAB, Data-Driven Science and Engineering trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the …
Data-DrivenOptimization - Stanford University
We want to choose an intermediate approach between stochastic optimization, which has no robustness to the error of distribution; and robust optimization, which ignores available …
Data-Driven Science and Engineering - Cambridge …
“Brunton and Kutz’s book is fast becoming an indispensable resource for machine learn-ing and data-driven learning in science and engineering. The second edition adds several timely topics …
Fundamentals Of Data Engineering (Download Only)
Importance of Data Engineering in Modern Businesses In today's data-driven world, data engineering is not just a mere cog in the machine; it is the driving force behind innovation, …
An Engineering Data - vols.wta.org
paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to …
Data-Driven Science and Engineering
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical …
Data-Driven Science and Engineering - Cambridge …
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical …
Data Driven Science And Engineering (book)
Data Driven Science And Engineering : Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning …
Data-Driven Engineering: Leveraging digital across the …
creating a dynamic, data-driven replica of infrastructure systems. These tools allow operators to simulate performance scenarios, test maintenance strategies over the full asset lifecycle.
Data Driven Science And Engineering (book)
Data Driven Science And Engineering : Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning …
Fundamentals of Data Engineering - api.pageplace.de
With this practical book, you’ll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the …
Introduction to Computational
Introduction to Computational Engineering with MATLAB® aims to teach readers how to use MATLAB® programming to solve numerical engineering problems. The book focuses on …
Data Driven Science And Engineering (book)
Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for modelling and control in engineering …
Data Driven Science And Engineering Copy
Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for modelling and control in engineering …
Data Driven Science And Engineering (2024)
Data Driven Science And Engineering: Data-Driven Science and Engineering Steven L. Brunton,J. Nathan Kutz,2022-05-05 A textbook covering data science and machine learning methods for …
