عنوان
پدید آورنده
موضوع
رده
TA404.3 .S77 2019eb
TA404
.
3
.
S77
2019eb
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
1315121069
(Number (ISBN
1351637878
(Number (ISBN
1351647385
(Number (ISBN
1498738214
(Number (ISBN
9781315121062
(Number (ISBN
9781351637879
(Number (ISBN
9781351647380
(Number (ISBN
9781498738217
Erroneous ISBN
1498738206
Erroneous ISBN
9781498738200
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Statistical methods for materials science :
General Material Designation
[Book]
Other Title Information
the data science of microstructure characterization /
First Statement of Responsibility
edited by Jeffrey P. Simmons, Charles A. Bouman, Marc De Graef, Lawrence F. Drummy, Jr.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Boca Raton, Florida :
Name of Publisher, Distributor, etc.
CRC Press,
Date of Publication, Distribution, etc.
[2019]
Date of Publication, Distribution, etc.
©2019
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
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?
Text of Note
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?
Text of Note
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
Text of Note
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
Text of Note
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
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8
8
8
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SUMMARY OR ABSTRACT
Text of Note
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.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Ingram Content Group
Stock Number
9781351647380
OTHER EDITION IN ANOTHER MEDIUM
International Standard Book Number
9781498738200
TOPICAL NAME USED AS SUBJECT
Materials science-- Mathematical models.
Materials science-- Statistical methods.
MATHEMATICS-- Probability & Statistics-- General.
SCIENCE-- Solid State Physics.
TECHNOLOGY & ENGINEERING-- Engineering (General)
TECHNOLOGY & ENGINEERING-- Reference.
TECHNOLOGY-- Material Science.
(SUBJECT CATEGORY (Provisional
MAT-- 029000
SCI-- 077000
TEC-- 009000
TEC-- 021000
TEC-- 035000
TGM
DEWEY DECIMAL CLASSIFICATION
Number
620
.
1/10727
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
TA404
.
3
Book number
.
S77
2019eb
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Bouman, Charles Addison
De Graef, Marc
Drummy, Lawrence F.
Simmons, Jeffrey P.
ORIGINATING SOURCE
Date of Transaction
20200822170907.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
