Statistical methods for materials science : the data science of microstructure characterization /
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| Other Authors: | , , , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, Florida :
CRC Press,
[2019]
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Cover; Half Title; Title Page; Copyright Page; Contents; Preface; About the Editors; Contributors; I Introduction; 1 Materials Science vs. Data Science; II Emerging Data Science in Microstructure Characterization; 2 Emerging Digital Data Capabilities; 2.1 Introduction; 2.2 Benefits of Large Data Volumes; 2.3 Challenges of Large Data Volumes; 2.4 Emerging Techniques; 2.4.1 Multi-Instrument Coordination; 2.4.2 Upstream Data Analysis; 2.4.3 Data Mining; 2.4.4 Data Curation; 2.5 Conclusions; 3 Cultural Differences; 3.1 What Makes Modern Image Processing So Modern?
- 3.2 Language of Image Processing3.2.1 Notational Differences; 3.2.1.1 Sets; 3.2.1.2 Operations on Sets; 3.2.1.3 Computations on Sets; 3.2.2 Bayesian Probability and Image Processing; 3.2.2.1 Modern Probability and Sets; 3.2.2.2 Foundational Rules of Modern Probability; 3.2.2.3 Mathematical Constructs; 3.2.2.4 Bayesian Probability in Image Processing; 3.3 Language of Materials Science; 3.3.1 Thermodynamic Phases; 3.3.2 Free Energies; 3.4 Concluding Remarks; 4 Forward Modeling; 4.1 What Is Forward Modeling?; 4.1.1 What Are the Unknowns in Materials Characterization?
- 4.1.2 A Schematic Description of Forward Modeling4.2 A Brief Overview of Electron Scattering Modalities; 4.3 Case Studies; 4.3.1 Electron Backscatter Diffraction; 4.3.1.1 BSE Monte Carlo Simulations; 4.3.1.2 Dynamical Scattering Simulations; 4.3.1.3 Detector Parameters; 4.3.2 Lorentz Vector Field Electron Tomography; 4.3.2.1 Lorentz Forward Model; 4.3.2.2 Electron Wave Phase Shift Computations; 4.3.2.3 Example Lorentz Image Simulation; 4.4 Summary; 5 Inverse Problems and Sensing; 5.1 Introduction; 5.2 Traditional Approaches to Inversion; 5.3 Bayesian and Regularized Approaches to Inversion
- 5.4 Why Does Bayesian Estimation Work?5.5 Model-Based Reconstruction; 5.6 Successes and Opportunities of Bayesian Inversion; III Inverse Methods for Analysis of Data; 6 Model-Based Iterative Reconstruction for Electron Tomography; 6.1 Introduction; 6.2 Model-Based Iterative Reconstruction; 6.3 High-Angle Annular Dark-Field STEM Tomography; 6.3.1 HAADF-STEM Forward Model; 6.3.2 Prior Model; 6.3.3 Cost Function Formulation and Optimization Algorithm; 6.3.4 Experimental Results; 6.3.4.1 Simulated Dataset; 6.3.4.2 Experimental Dataset; 6.4 Bright-Field Electron Tomography
- 6.4.1 BF-TEM Forward Model and Cost Function Formulation6.4.1.1 Generalized Huber Functions for Anomaly Modeling; 6.4.1.2 MBIR Cost Formulation; 6.4.2 Results; 6.4.2.1 Simulated Dataset; 6.4.2.2 Real Dataset; 6.5 Future Directions; 6.6 Conclusion; 7 Statistical Reconstruction and Heterogeneity Characterization in 3-D Biological Macromolecular Complexes; 7.1 Introduction; 7.2 Statistical 3-D Signal Reconstruction of Macromolecular Complexes; 7.2.1 Introduction; 7.2.2 Statistical Model; 7.2.3 Relationship between the Moments of the Weights and the Moments of the Electron Scattering Intensity