About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
A Hybrid Gaussian Random Field – Deep Learning Model for Statistically Controllable Synthetic Microstructure Generation |
Author(s) |
Andreas E. Robertson, Conlain Kelly, Michael Buzzy, Surya Kalidindi |
On-Site Speaker (Planned) |
Andreas E. Robertson |
Abstract Scope |
Recently, we proposed a Multi-Output Gaussian Random Field (GRF) model for statistically controllable synthetic microstructure generation. Because first and second order microstructure statistics are directly incorporated into the model’s construction, the model can be used to generate synthetic microstructures from arbitrary microstructure processes. Although it can stably extrapolate to previously unobserved statistics without the need for training data, its ability to generate realistic microstructures is limited by its rigid higher-order statistics. In this talk, expanding on this original model, we develop a hybrid deep-learning model that combines the first and second order statistical parameterization of the GRF model with the expressive higher order statistics of deep learning models. Importantly, we demonstrate that using this hybrid approach, the model can be effectively trained even in Materials Informatics’ characteristic data-starved learning environments. After presenting the model structure and necessary algorithms, we explore several important case studies to emphasize its strengths and weaknesses. |
Proceedings Inclusion? |
Planned: |
Keywords |
ICME, Machine Learning, Other |