About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Adversarial Hierarchical Variational Autoencoder: A Novel Autoencoder Architecture for Microstructure Synthesis and Feature Extraction |
Author(s) |
Simon Mason, Mengfei Yuan, Ashley Lenau, Octavian Donca, Dennis Dimiduk, Steve Niezgoda |
On-Site Speaker (Planned) |
Simon Mason |
Abstract Scope |
Because of the high cost of experimental 3D microstructure data, computational methods of microstructure synthesis have been established that allow large-scale dataset generation, including phase-field models, DREAM.3D, and neural network architectures. The present work explores development of a novel autoencoder, the adversarial hierarchical variational autoencoder (AHVAE), that combines the benefits of the traditional VAE network, the hierarchical nature of the Nouveau VAE’s latent spaces, and the improved image generation capabilities of adversarial training. Along with its use as a microstructure generator, the AHVAE can also be used as a feature extractor through the utilization of the information-dense latent space. After training the full network, the encoder can be used independently to generate low-dimensional latent space representations of microstructures. These latent spaces can then be used within separate, specifically-trained decoders to extract a variety of desired material features, including physical parameter extraction, microstructure topology analysis, and material property prediction. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, |