Statistical Methods for Materials Science
The Data Science of Microstructure Characterization
1st Edition
Published on 6. February 2019
Book
Hardback
536 pages
978-1-4987-3820-0 (ISBN)
Description
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.
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Illustrations
215 s/w Abbildungen, 19 s/w Tabellen
19 Tables, black and white; 215 Illustrations, black and white
Dimensions
Height: 254 mm
Width: 178 mm
Weight
1102 gr
ISBN-13
978-1-4987-3820-0 (9781498738200)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jeffrey P. Simmons | Lawrence F. Drummy | Charles A. Bouman
Statistical Methods for Materials Science
The Data Science of Microstructure Characterization
Book
03/2021
1st Edition
CRC Press
€71.90
Shipment within 15-20 days
Jeffrey P. Simmons | Lawrence F. Drummy | Charles A. Bouman
Statistical Methods for Materials Science
The Data Science of Microstructure Characterization
E-Book
02/2019
1st Edition
CRC Press
€66.99
Available for download
Jeffrey P. Simmons | Lawrence F. Drummy | Charles A. Bouman
Statistical Methods for Materials Science
The Data Science of Microstructure Characterization
E-Book
02/2019
1st Edition
CRC Press
€66.99
Available for download
Persons
Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef
Content
1 Materials Science vs. Data Science 2 Emerging Digital Data Capabilities 3 Cultural Differences 4 Forward Modeling 5 Inverse Problems and Sensing 6 Model-Based Iterative Reconstruction for Electron Tomography 7 Statistical reconstruction and heterogeneity characterization in 3-D biological macromolecular complexes 8 Object Tracking through Image Sequences 9 Grain Boundary Characteristics 10 Interface Science and the Formation of Structure 11 Hierarchical Assembled Structures from Nanoparticles 12 Estimating Orientation Statistics 13 Representation of Stochastic Microstructures 14 Computer Vision for Microstructure Representation 15 Topological Analysis of Local Structure 16 Markov Random Fields for Microstructure Simulation 17 Distance Measures for Microstructures 18 Industrial Applications 19 Anomaly Testing 20 Anomalies in Microstructures 21 Denoising Methods with Applications to Microscopy 22 Compressed Sensing for Imaging Applications 23 Dictionary Methods for Compressed Sensing 24 Sparse Sampling in Microscopy