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