|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Synthesizing Realistic Images of Material Microstructures Using Convolutional Neural Networks
||Stephen Baek, H.S. Udaykumar, WaiChing Sun, Phong Nguyen
|On-Site Speaker (Planned)
Synthetic images of microstructures facilitate the design of functional materials by enabling the exploration of design space. Numerical experiments can be conducted on synthetically generated microstructures with controlled morphologies, to assimilate linkages between microstructural morphology and physical quantities of interest. However, traditional ways of generating synthetic microstructures are limited to some idealized shapes, such as spheres and polyhedrals, and significantly lack realism, which limits the use of synthetic microstructures to simple trend analyses. To this end, machine learning approaches have rapidly emerged as feasible alternatives. Especially, the unprecedented capacity of convolutional neural networks (CNN) in machine cognition and image synthesis has created exciting new frontiers in materials research. In this talk, we present some of our recent case studies on the use of CNNs for microstructure image synthesis, focusing on the materials-by-design of energetic materials.
||Machine Learning, Modeling and Simulation, Computational Materials Science & Engineering