|About this Abstract
||MS&T21: Materials Science & Technology
||AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
||A Deep Generative Model for Parametric EBSD Pattern Simulation
||Zihao Ding, Marc De Graef
|On-Site Speaker (Planned)
Currently, the mainstream approach for electron backscatter diffraction (EBSD) pattern simulation is through a physics-based forward model, which calculates backscattered electron trajectories using Monte Carlo and dynamical scattering simulations and then generates patterns using a gnomonic projection. For each simulation, the result is specific to the given set of parameters. It has been shown that a deep neural network is able to extract features from EBSD patterns and predict various characteristics. We propose a deep generative model that combines a conditional variational autoencoder with a generative adversarial network to realize analytic and parametric EBSD pattern simulation. Compared with the conventional forward model, the deep generative model summarizes a distribution over multiple parameters. The accuracy and quality of the generated patterns can be analyzed by accepted indexing methods.