About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Data-driven Surrogate Models for Predicting Microstructural Evolution |
Author(s) |
Peichen Wu, Kumar Ankit, Ashif Sikandar Lquebal |
On-Site Speaker (Planned) |
Peichen Wu |
Abstract Scope |
Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, for accuracy, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing. Although recently developed surrogate models demonstrate the viability of deep-learning techniques in predicting microstructural evolution, such approaches require enormous amounts of high-fidelity training data while primarily relying on principal component analyses for microstructure representation, where spatiotemporal information is lost in the pursuit of dimensionality reduction. Given these limitations, we present a novel data-driven emulator (DDE) for predicting microstructural evolution which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. The efficacy of our microstructure emulation technique will be discussed in the context of modeling microphase separation at the mesoscale. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Modeling and Simulation, Other |