Statistical Methods for Materials Science : The Data Science of Microstructure Characterization book cover
1st Edition

Statistical Methods for Materials Science
The Data Science of Microstructure Characterization

ISBN 9781498738200
Published February 6, 2019 by CRC Press
514 Pages - 215 B/W Illustrations

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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.

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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Jeffrey P. Simmons is a Scientist with the Materials and Manufacturing Directorate of the Air Force Research Laboratory (AFRL). He received the B.S. degree in metallurgical engineering from the New Mexico Institute of Mining and Technology, Socorro, NM, USA, and M.E. and Ph.D. degrees in Metallurgical Engineering and Materials Science and Materials Science and Engineering, respectively, from Carnegie Mellon University, Pittsburgh, PA, USA. After receiving the Ph.D. degree, he began work at AFRL as a post-doctoral research contractor. In 1998, he joined AFRL as a Research Scientist. His research interests are in computational imaging for microscopy and has developed advanced algorithms for analysis of large image datasets. Other research interests have included phase field (physics-based) modeling of microstructure formation, atomistic modeling of defect properties, and computational thermodynamics. He has lead teams developing tools for digital data analysis and computer resource integration and security. He has overseen execution of research contracts on computational materials science, particularly in prediction of machining distortion, materials behavior, and thermodynamic modeling. He has published in both the Materials Science and Signal Processing fields. He is a member of ACM and a senior member of IEEE.

Charles A. Bouman received a B.S.E.E. degree from the University of Pennsylvania in 1981 and a MS degree from the University of California at Berkeley in 1982. From 1982 to 1985, he was a full staff member at MIT Lincoln Laboratory and in 1989 he received a Ph.D. in electrical engineering from Princeton University. He joined the faculty of Purdue University in 1989 where he is currently the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering. Professor Boumans research is in statistical signal and image processing in applications ranging from medical to scientific and consumer imaging. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tom ography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products.

Marc De Graef received his BS and MS degrees in physics from the University of Antwerp (Belgium) in 1983, and his Ph.D. in physics from the Catholic University of Leuven (Belgium) in 1989, with a thesis on copper-based shape memory alloys. He then spent three and a half years as a post-doctoral researcher in the Materials Department at the University of California at Santa Barbara before joining Carnegie Mellon in 1993 as an assistant professor. He is currently professor and codirector of the J. Earle and Mary Roberts Materials Characterization Laboratory. His research interests lie in the area of microstructural characterization of structural intermetallics and magnetic materials and include the development of numerical techniques to model a variety of materials characterization modalities. Prof. De Graef has published two text books and more than 280 publications.

Lawrence F. Drummy Jr. is a senior materials engineer in the Soft Matter Materials Branch, Functional Materials Division, Materials and Manufacturing Directorate, Air Force Research Laboratory in Dayton, OH. Dr. Drummy received his BS in Physics at Rensselaer Polytechnic Institute while researching scanning tunneling microscopy and image processing of silicon growth on surfaces. In 2003 he received his PhD from the Department of Materials Science and Engineering at the University of Michigan while performing research on defect structures in organic molecular semiconductor thin films for flexible electronics. Dr. Drummys research interests include three dimensional morphology characterization of biological, polymeric and nanostructured materials, the structure of materials at interfaces, and data analytics for materials science applications such as microscopy.


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