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Statistical Methods for Materials Science

Statistical Methods for Materials Science

Publisher Productivity Press Inc
Year 01/02/2019
Edition First
Pages 514
Version hardback
Readership level College/higher education
Language English
ISBN 9781498738200
Categories Probability & statistics, Condensed matter physics (liquid state & solid state physics), Materials science
$314.43 (with VAT)
1 250.54 PLN / €290.71 / £243.80
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Book 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.

Statistical Methods for Materials Science

Table of contents

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

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