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

Jeffrey P. Simmons (Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio, USA), Lawrence F. Drummy (Carnegie Mellon University, Materials Science and Engineering Department, Pittsburgh, Pennsylvania, USA), Charles A. Bouman (Purdue University, ECE and Biomedical Engineering, West Lafayette, Indiana, USA), Marc De Graef (Carnegie Mellon University, Department of Materials Science and Engineering, Pittsburgh, Pennsylvania, USA)

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.

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