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Applied Data Analysis And Modeling For Energy Engineers And Scientists [PDF Download]

Applied Data Analysis and Modeling for Energy Engineers and Scientists PDF
Author: T. Agami Reddy
Publisher: Springer Science & Business Media
Release Date: 2011-08-09
ISBN: 9781441996138
Size: 36.95 MB
Format: PDF, ePub, Docs
Category : Technology & Engineering
Languages : en
Pages : 430
View: 5078

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Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools.

Energy Research Abstracts [PDF Download]

Energy Research Abstracts PDF
Author:
Publisher:
Release Date: 1990
ISBN:
Size: 80.88 MB
Format: PDF, ePub, Mobi
Category : Power resources
Languages : en
Pages :
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Applied Statistical Modeling And Data Analytics [PDF Download]

Applied Statistical Modeling and Data Analytics PDF
Author: Srikanta Mishra
Publisher: Elsevier
Release Date: 2017-10-27
ISBN: 0128032804
Size: 11.13 MB
Format: PDF, ePub
Category : Science
Languages : en
Pages : 250
View: 6283

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Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains Written by practitioners for practitioners Presents an easy to follow narrative which progresses from simple concepts to more challenging ones Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications

Computational Intelligent Data Analysis For Sustainable Development [PDF Download]

Computational Intelligent Data Analysis for Sustainable Development PDF
Author: Ting Yu
Publisher: CRC Press
Release Date: 2016-04-19
ISBN: 1439895953
Size: 51.25 MB
Format: PDF, ePub
Category : Business & Economics
Languages : en
Pages : 440
View: 6240

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Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present

Deep Learning Techniques And Optimization Strategies In Big Data Analytics [PDF Download]

Deep Learning Techniques and Optimization Strategies in Big Data Analytics PDF
Author: Thomas, J. Joshua
Publisher: IGI Global
Release Date: 2019-11-29
ISBN: 1799811948
Size: 21.36 MB
Format: PDF
Category : Computers
Languages : en
Pages : 355
View: 6238

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Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Data Science Applied To Sustainability Analysis [PDF Download]

Data Science Applied to Sustainability Analysis PDF
Author: Jennifer Dunn
Publisher: Elsevier
Release Date: 2021-05-01
ISBN: 0128179775
Size: 31.20 MB
Format: PDF, ePub, Docs
Category : Science
Languages : en
Pages : 300
View: 4074

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Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery Includes considerations sustainability analysts must evaluate when applying big data Features case studies illustrating the application of data science in sustainability analyses