Statistical Methods For Materials Science
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Statistical Methods For Materials Science
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Author : Jeffrey P. Simmons
language : en
Publisher: CRC Press
Release Date : 2019-02-13
Statistical Methods For Materials Science written by Jeffrey P. Simmons and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-13 with Technology & Engineering categories.
Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.
Statistical Analysis Of Microstructures In Materials Science
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Author : Joachim Ohser
language : en
Publisher: John Wiley & Sons
Release Date : 2000-12-19
Statistical Analysis Of Microstructures In Materials Science written by Joachim Ohser and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-12-19 with Mathematics categories.
The investigation of the origin and formation of microstructures and the effect that microstructure has on the properties of materials are important issues in materials science and technology. Geometrical analysis is often the key to understanding the formation of microstructures and the resulting material properties. The authors make use of mathematical morphology, spatial statistics, image processing, stereology and stochastic geometry to analyze microstructures arising in materials science. * Quantitative microstructure analysis is one of the most successful experimental techniques in materials science * Uses examples to demonstrate the techniques * Program code included enables the reader to apply the numerous algorithms * Accessible to material scientists with limited statistical knowledge Primarily aimed at applied materials scientists, the book will also appeal to those working and researching in earth sciences, material technology, mineralogy, petrography, image analysis, cytology and biology.
Statistical And Multivariate Analysis In Material Science
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Author : Taylor & Francis Group
language : en
Publisher: CRC Press
Release Date : 2022-11-15
Statistical And Multivariate Analysis In Material Science written by Taylor & Francis Group and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-15 with categories.
"This book introduces the reader to univariate and multivariate statistics applied to material science in an easy non-mathematical approach. Contains several case studies and tutorials in order to help readers apply the techniques described in this book on their own data. The book will interest scientists and advanced students specializing in material science, corrosion science, chemometrics"--
Statistical Methods In Materials Research
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Author : Pennsylvania State University. Dept. of Engineering Mechanics
language : en
Publisher:
Release Date : 1956
Statistical Methods In Materials Research written by Pennsylvania State University. Dept. of Engineering Mechanics and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1956 with Materials categories.
Nonparametric Statistics With Applications To Science And Engineering
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Author : Paul H. Kvam
language : en
Publisher: John Wiley & Sons
Release Date : 2007-08-24
Nonparametric Statistics With Applications To Science And Engineering written by Paul H. Kvam and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-24 with Mathematics categories.
A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.
Bayesian Optimization For Materials Science
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Author : Daniel Packwood
language : en
Publisher: Springer
Release Date : 2017-10-04
Bayesian Optimization For Materials Science written by Daniel Packwood and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-04 with Technology & Engineering categories.
This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science.Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While research in these directions has been reported in high-profile journals, until now there has been no textbook aimed specifically at materials scientists who wish to incorporate Bayesian optimization into their own research. This book will be accessible to researchers and students in materials science who have a basic background in calculus and linear algebra.
Information Science For Materials Discovery And Design
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Author : Turab Lookman
language : en
Publisher: Springer
Release Date : 2015-12-12
Information Science For Materials Discovery And Design written by Turab Lookman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-12 with Technology & Engineering categories.
This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ to our toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics.
Statistical Methods For Communication Science
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Author : Andrew F. Hayes
language : en
Publisher: Routledge
Release Date : 2020-10-14
Statistical Methods For Communication Science written by Andrew F. Hayes and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-14 with Language Arts & Disciplines categories.
Statistical Methods for Communication Science is the only statistical methods volume currently available that focuses exclusively on statistics in communication research. Writing in a straightforward, personal style, author Andrew F. Hayes offers this accessible and thorough introduction to statistical methods, starting with the fundamentals of measurement and moving on to discuss such key topics as sampling procedures, probability, reliability, hypothesis testing, simple correlation and regression, and analyses of variance and covariance. Hayes takes readers through each topic with clear explanations and illustrations. He provides a multitude of examples, all set in the context of communication research, thus engaging readers directly and helping them to see the relevance and importance of statistics to the field of communication. Highlights of this text include: *thorough and balanced coverage of topics; *integration of classical methods with modern "resampling" approaches to inference; *consideration of practical, "real world" issues; *numerous examples and applications, all drawn from communication research; *up-to-date information, with examples justifying use of various techniques; and *downloadable resources with macros, data sets, figures, and additional materials. This unique book can be used as a stand-alone classroom text, a supplement to traditional research methods texts, or a useful reference manual. It will be invaluable to students, faculty, researchers, and practitioners in communication, and it will serve to advance the understanding and use of statistical methods throughout the discipline.
Materials Science And Engineering
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Author : Krishna Rajan
language : en
Publisher: Elsevier Inc. Chapters
Release Date : 2013-07-10
Materials Science And Engineering written by Krishna Rajan and has been published by Elsevier Inc. Chapters this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-10 with Technology & Engineering categories.
This chapter provides an introduction to how data-mining techniques can help to correlate multiple modalities of signals to extract information from images. The discussion is built around two genres of applications of imaging/spectral informatics. One is exploration of the role of informatics to enhance the resolution of detection of spatial correlations in chemistry through examples of chemical imaging techniques based on electron energy loss spectroscopy (EELS) and cathodoluminescence (CL). These case studies serve to highlight the use of data dimensionality reduction techniques to link the multiple modalities of imaging contrast that serve to enhance the “information contrast” that helps to uncover subtle but important spatial correlations in chemistry. The other set of case studies is built around the use of Fourier transform infrared spectroscopy (FTIR). These examples serve to highlight a different role in the application of informatics methods in materials characterization, namely to track changes in physical/structural properties of materials associated with processing variations. Here it is demonstrated how data dimensionality reduction methods can uncover correlations between features in spectra that cannot be detected by visual inspection. Hence spectral informatics provides a methodology to monitor structural and chemical pathways that govern processing–property relationships in materials.
Materials Science And Engineering
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Author : S. Samudrala
language : en
Publisher: Elsevier Inc. Chapters
Release Date : 2013-07-10
Materials Science And Engineering written by S. Samudrala and has been published by Elsevier Inc. Chapters this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-10 with Technology & Engineering categories.
Materials science research has witnessed an increasing use of data-mining techniques in establishing structure–process–property relationships. Significant advances in high-throughput experiments and computational capability have resulted in the generation of huge amounts of data. Various statistical methods are currently employed to reduce the noise, redundancy, and dimensionality of the data to make analysis more tractable. Popular methods for reduction (such as principal component analysis) assume a linear relationship between the input and output variables. Recent developments in nonlinear reduction (neural networks, self-organizing maps), though successful, have computational issues associated with convergence and scalability. This chapter reviews various spectral-based techniques that efficiently unravel linear and nonlinear structures in the data, which can subsequently be used to tractably investigate structure–property–process relationships. We compare and contrast the advantages and disadvantages of these techniques and discuss the mathematical and algorithmic underpinning of these methods. In addition, we describe techniques (based on graph-theoretic analysis) to estimate the optimal dimensionality of the low-dimensional parametric representation. We show how these techniques can be packaged into a modular, computationally scalable software framework with a graphical user interface – Scalable Extensible Toolkit for Dimensionality Reduction (SETDiR). This interface helps to separate out the mathematics and computational aspects from the material science applications, thus significantly enhancing utility to the materials science community. The applicability of the framework in constructing reduced order models of complicated materials data sets is illustrated.