1st Edition

Statistical Methods for Materials Science The Data Science of Microstructure Characterization

    536 Pages 215 B/W Illustrations
    by CRC Press

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    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.

    Chapter 1 Materials Science vs. Data Science

    Jeff Simmons, Lawrence Drummy, Charles Bouman, Marc De Graef

    Chapter 2 Emerging Digital Data Capabilities

    Stephen Mick

    Chapter 3 Cultural Differences

    Mary Comer, Charles Bouman, Jeff Simmons

    Chapter 4 Forward Modeling

    Marc De Graef

    Chapter 5 Inverse Problems and Sensing

    Charles Bouman

    Chapter 6 Model-Based Iterative Reconstruction for Electron Tomography

    Singanallur Venkatakrishnan, Lawrence Drummy

    Chapter 7 Statistical reconstruction and heterogeneity characterization in 3-D biological macromolecular complexes

    Qiu Wang, Peter C. Doerschuk

    Chapter 8 Object Tracking through Image Sequences

    Song Wang, Hongkai Yu, Youjie Zhou, Jeff Simmons, Craig Przybyla

    Chapter 9 Grain Boundary Characteristics

    Hossein Beladi, Gregory S. Rohrer

    Chapter 10 Interface Science and the Formation of Structure

    Ming Tang, Jian Luo

    Chapter 11 Hierarchical Assembled Structures from Nanoparticles

    Dhriti Nepal, Sushil Kanel, Lawrence Drummy

    Chapter 12 Estimating Orientation Statistics

    Stephen R. Niezgoda

    Chapter 13 Representation of Stochastic Microstructures

    Stephen R. Niezgoda

    Chapter 14  Computer Vision for Microstructure Representation

    Brian DeCost, Elizabeth Holm

    Chapter 15 Topological Analysis of Local Structure

    Emanuel Lazar, David Srolovitz

    Chapter 16 Markov Random Fields for Microstructure Simulation

    Veera Sundararaghavan

    Chapter 17 Distance Measures for Microstructures

    Patrick Callahan

    Chapter 18 Industrial Applications

    David Furrer, David Brough, Ryan Noraas

    Chapter 19 Anomaly Testing

    James Theiler

    Chapter 20 Anomalies in Microstructures

    Stephen Bricker, Craig Przybyla, Jeff Simmons, Russel Hardie

    Chapter 21 Denoising Methods with Applications to Microscopy

    Rebecca Willett

    Chapter 22 Compressed Sensing for Imaging Applications

    Justin Romberg

    Chapter 23 Dictionary Methods for Compressed Sensing

    Saiprasad Ravishankar, Raj Rao Nadakuditi

    Chapter 24 Sparse Sampling in Microscopy

    Kurt Larson, Hyrum Anderson, Jason Wheeler



    Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef