About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Data Driven Microstructure Evolution: Adjusting Growth Speed and Anisotropy |
Author(s) |
Joseph Melville, Amanda Krause, Joel Harley, Weishi Yan, Lin Yang, Michael Tonks |
On-Site Speaker (Planned) |
Joseph Melville |
Abstract Scope |
Microstructural grain growth simulations are used to predict microstructure evolution over time. Conventional grain growth simulations are based on many available techniques, including phase field, cellular automata, and Monte-Carlo-Potts methods. Yet, the rigid physics-based assumptions of these methods have difficulty capturing some real world grain growth behaviors, such as abnormal grain growth. As a result, it can be difficult to adapt these simulations with experimental knowledge. This presentation discusses a physics-informed deep learning framework for microstructural grain growth simulation. This hybrid model enables us to train microstructure evolution based on a combination of physical laws and data (from simulations or experiments). This presentation specifically highlights how this framework learns to adjust growth speed and anisotropy from data. In addition, due to our integration of physics, this adaptive process does not require huge amounts of new data. |
Proceedings Inclusion? |
Undecided |