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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Adversarial Networks for Digital Microstructure Generation
Author(s) Stephen R. Niezgoda, Mengfei Yuan, Dennis Dimiduk, Yunzhi Wang
On-Site Speaker (Planned) Stephen R. Niezgoda
Abstract Scope The microstructures of engineering materials are complex, having detailed crystallography, morphology, and composition. Their responses, especially to extreme-value limited stimuli, depend upon specific aspects of microstructure. Thus, highly-accurate virtual representations are needed as inputs to for modeling and simulation. The challenge to applying data science techniques to microstructure generation is that large amount of training data is typically requires, which is largely intractable due to the large cost of collecting high fidelity microstructural images. In this talk we explore the application of Generative Adversarial Networks (GAN) and related techniques to the generation of digital materials. GANs consist of two independent networks a generator, which creates trial synthetic microstructures, and a discriminator which seeks to identify the synthetic from real images. The two networks learn from each other and by engaging in competition can achieve superior performance, with far less training data, than other deep learning approaches.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Nanoprinting: An Integrated Approach of Experiments, Computer-aided Design and Simulations
4D STEM Data Acquisition, Analytics and Functional Material Property Extraction
A Machine Learning Approach to Independent Component Analysis for Nuclear Magnetic Resonance Spectra
Adversarial Networks for Digital Microstructure Generation
Application of a Statistical Analysis Technique for Characterizing the Deformation Behavior of the Material under Dynamic Impact Loading
Application of Artificial Neural Networks to Low Cycle Fatigue and Creep Data Processing for Power Plant Materials
Automated Defect Detection in Electron Microscopy with Machine Learning
Data Analytics for Correlative Multimodal Chemical and Functional Imaging
Data Science and the MGI
Deciphering the Atomic Origin of Glasses’ Properties by Machine Learning
Deep Learning and MC-X ray, toward Automatic Sample Segmentation
Digital Protocols for Statistical Quantification of Microstructure Features in Polycrystalline Nickel-based Superalloys
Human-in-the-loop Strategies for Dimensionality Reduction and Optimization in Materials Design
Model-based Reconstruction Algorithms for Time-of-Flight Neutron Tomography
Modeling and Simulation of Rare Events in Multidimensional Spaces
Multi-modal Data Fusion and 3D Reconstruction of Serial Sectioning Data
Neural Networks for Processing of Low Signal-to-noise Data in Scanning Probe Microscopy
P2-8: Evaluation for the Quality of Flake Graphite Cast Iron and Spheroidal Graphite Cast Iron by Tapping Test with Using Artificial Intelligence
Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
Recent Advances in 3D Reconstruction Based on Spherical Indexing of EBSD Data
Structure Prediction and Property-based Optimization of Molecular Crystals with GAtor
Workflows for Curation and Analysis of Microstructure-Aware Materials Data: Application to Aging of U-Nb Alloys

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