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Meeting 2014 TMS Annual Meeting & Exhibition
Symposium Data Analytics for Materials Science and Manufacturing
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Jeff Simmons, Air Force Research Laboratory
Charles Bouman, Purdue University
Fariba Fahroo, Air Force Office of Scientific Research
Surya R. Kalidindi, Georgia Institute of Technology
Jeremy Knopp, Air Force Research Laboratory
Peter W. Voorhees, Northwestern Univ
Scope A prominent 'grand challenge' for the 21st century is to effect a reversal of the paradigm by which new materials are developed and manufactured, especially to address the stringent demands of applications in advanced technologies. It is evident that the time and resource consumptive empiricism that has dominated materials development during the past century, must give way to a greater dependence on modeling and simulation and modern data science tools. Materials Science has not escaped being impacted by the challenges of Big Data. In fact, the unique combination of automated data collection on almost every instrument from microscopes to mechanical property characterization and the unusually well-developed physics-based state evolution models that we normally think of as Computational Materials Science, our field is expected to experience an explosion progress in the coming years. With the data available, on the one hand, from automated data collection and, on the other, from physics-based models, Materials Science is set to be a major player in the Big Data sphere. Data Analytics, the new field that is growing to provide the scientific basis of converting all of this data to information that can be used in critical decision making, is expected to be a key technology in the future. This symposium will bring together all facets of the nascent Data Analytics for Materials Science and Manufacturing field in order to facilitate communication and collaboration among the various camps. Papers will be sought on all subjects related to Data Analytics, a non-inclusive set of topics is: fusion of multimodal data involving different sensors characterizing the same sample, inverse methods for reconstruction of geometric, internal stress, chemistry and other features, forward modeling of microscope observations, uncertainty quantification of computed results and the effects of experimental uncertainties, graph theoretic methods for representing materials structures, ontologies for representing materials characteristics and properties, methods for parameter estimation in physics-based models, segmentation and subsequent analysis of microscope data, compressed sensing in data acquisition, data driven modeling, dimensionality reduction methods such as reduced order models or manifold learning methods, end member analysis of multi- and hyper-spectral data, prediction of rare events, anomaly detection, decision making under uncertainty, and data mining in Materials Science.
Abstracts Due 07/15/2013
Proceedings Plan Planned: A print-only volume

2D Stochastic-integral Models for Characterizing Random Grain Noise in Titanium Alloys
2D/3D Data Registration and Fusion
A Data Mining Approach in Structure-Property Optimization
A Markov Random Field Approach for Microstructure Synthesis
A New Probabilistic Graph Model and Its Application to Materials Microstructures
A Novel Method for Automated Quantification of Particles in Solidified Aluminium
A Response Surface Method (RSM) for Model-based Optimization of Expanded Perlite Production
An ICME Example in Production: ICME-net
Analyzing and Mining Materials Data: From Academia to Industry
Applications of Wavelets in the Representation and Prediction of Transformation in Shape-memory Polycrystals
Autonomous Research Systems for Materials Science
Bayesian Inference of Grain Boundary Properties from Heterogeneous Data
Comparison of Novel Microstructure Quantification Frameworks for Visualization, and Analysis of Microstructure Data
Compressed Sensing for Fast Electron Microscopy
Data Analysis and Quantification of 3D Microstructures
Data Analytics for Residual Stress in Materials
Data Topology as a Framework for Materials Discovery and Material Mimetic Design
Dictionary-based Diffraction Microscopy for Materials
Effective Extraction of Both Impurity Diffusion Coefficients and Interdiffusion Coefficients for Diffusivity Database Establishment
Forward Modeling of Electron Microscopy
Foundational Engineering Problem: Uncertainty Quantification in Multi-disciplinary Analysis of Bulk Residual Stresses in Disks
Fully Automated, High-throughput Powder X-ray Data Analysis
Grain Boundary Data as a Big Data Problem
Growth Path Envelope Analysis of Grain Growth in Tungsten
Hyperspectral Image Analysis: From Qualitative to Quantitative Analysis
Integrated Material Characterization Property Prediction Using 3D Image-based Analytics and Modeling
Leveraging Data Science to Enable Multiscale Materials Modeling and Design
Linking 3D X-ray Imaging and Simulations
Model-based Iterative Reconstruction for Multimodal Electron Tomography
Modeling Direct and Inverse Problems in Ferritic Heat-exchanger Tubes
New Data Mining Techniques in Materials Science : Bayesian Networks to Predict the Yield Stress of Ni-Base Superalloys
Not Your Father's Topology: Modern Views of Connectivity in Grain Structures
Phase-based Property Data Informatics
Physics-based Models for Information Processing with Applications to Materials Characterization
Physics of Regularized Image Processing
Predictive Modeling in Characterizing Localization Relationships
Quantifying the Similarity between Two Microstructures
Rapid Ideation, Modeling and Simulation in a Collaborative Crowdsourcing Environment for Evolutionary Design (CEED)
Scalable Graph-based Techniques for Large-scale Materials Data
Stochastic-integral Models for Propagation-of-uncertainty Problems in Nondestructive Evaluation
The Challenge of Combining Massive, High-dimensionality Data Streams from the Atomscope
The MGI and the Role of Theory
Toward the Minimal Set of Morphological Information for Statistical Material Microstructure Modeling
Virtual Analysis of Experimental Techniques for Determining Grain Volume Distribution and Number per Unit Volume

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