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Statistical Methods for Materials Science: The Data Science of Microstructure Characterization

Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for;

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Statistical Methods for Materials Science

Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for;

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Concise Encyclopedia of Materials Characterization

. The second edition of Concise Encyclopedia of Materials Characterization covers the wide range of characterization techniques necessary to;

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Statistical Analysis of Microstructures in Materials Science

algorithms Accessible to material scientists with limited statistical knowledge Primarily aimed at applied materials scientists, the book will also;

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Concise Dictionary of Materials Science

A detailed knowledge of the terminology and its background is necessary for a fundamental understanding of the professional literature;

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Concise Dictionary of Materials Science

A detailed knowledge of the terminology and its background is necessary for a fundamental understanding of the professional literature;

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Random Heterogeneous Materials: Microstructure and Macroscopic Properties

deals with the quantitative characterization of the microstructure of heterogeneous via theoretical methods; Part II treats a wide variety;

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Computational Methods for Microstructure-Property Relationships

Computational Methods for Microstructure-Property Relationships introduces state-of-the-art advances in computational modeling approaches;

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Processing and Properties of Advanced Ceramics and Composites III

This book contains 17 papers from the Innovative Processing and Synthesis of Ceramics, Glasses and Composites and Advances in Ceramic;

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Characterization and Control of Interfaces for High Quality Advanced Materials

Interface characterization and control are critical in the design and manufacture of high quality advanced materials, particularly, for;

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Statistics for Data Scientists

methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a;

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Practical Statistics for Data Scientists

belongs to Statistical machine learning methods that learn from data Unsupervised learning methods for extracting meaning from unlabeled data;

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Neutrons and Synchrotron Radiation in Engineering Materials Science

to the fundamentals of structure-property relationships in materials and the radiation sources suitable for materials characterization. The;

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Monatomic Two-Dimensional Layers

is followed by advanced science specific to the system. Characterization techniques, the principles of state-of-the-art instruments for monatomic;

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Processing and Characterization of Multicomponent Polymer Systems

and Characterization of Multicomponent Polymer Systems is a collection of contributions from materials science experts across the globe. The;

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Processing and Characterization of Multicomponent Polymer Systems

and Characterization of Multicomponent Polymer Systems is a collection of contributions from materials science experts across the globe. The;

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Characterization of Minerals, Metals, and Materials 2021

(SEM, FIB, TEM), and spectroscopy (EDS, WDS, EBSD) techniques. * 2D and 3D modelling for materials characterization. The;

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Mathematical Problems in Data Science

and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets;

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Data Science for Nano Image Analysis

This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type;

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Data Science for Nano Image Analysis

This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type;

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Plastics

Plastics: Microstructure and Applications is a key text for senior students studying the science and engineering of plastics materials;

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Machine Learning for Subsurface Characterization

, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use;

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Microstructural Characterization Of Materials

experimental methods used for this purpose. Similar to the first edition, this 2nd edition explores the methodology of materials characterization under;

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Statistical Foundations, Reasoning and Inference

and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the;

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Combinatorial Materials Science

with informatics approaches to the analysis of Combinatorial Science (CombiSci) data. CombiSci has been used extensively in the pharmaceutical;

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Applied Statistics for Environmental Science with R

science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and;

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Information Science for Materials Discovery and Design

and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery;

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