1st Edition
Statistical Methods for Materials Science The Data Science of Microstructure Characterization
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.
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
Biography
Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef
