About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Physics-Based Deep Learning Methods for Enforcing Stress Equilibrium in GAN Generated Stress Fields |
Author(s) |
Ashley Lenau, Dennis Dimiduk, Stephen Niezgoda |
On-Site Speaker (Planned) |
Ashley Lenau |
Abstract Scope |
Deep learning (DL) is an increasingly growing field in computational materials science with advancements in modeling the structure-property relationships of materials, material design, and microstructure generation. However, large amounts of data are typically required to sufficiently train a DL network, and acquiring this amount can be a difficult task in materials science. Incorporating physics-based regularization methods into DL algorithms can possibly speed up the time and reduce the data needed to train a DL network. In this work, an image translation generative adversarial network is used to translate the elastic stress fields from a high-contrast composite image. Various physics-based regularization methods are used to capture high-frequency features and to enforce a stress equilibrium constraint on the elastic stress fields. In this presentation, the time and data needed to adequately train the DL model having physics-based regularization methods are shown in comparison to the DL model without these methods. |
Proceedings Inclusion? |
Planned: |
Keywords |
Mechanical Properties, Machine Learning, Computational Materials Science & Engineering |