EAGG-UF/PRIMME
The repository for the Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME) framework for learning and emulating microstructure grain growth.
This tool helps materials scientists and engineers predict and understand how microstructures, specifically grain growth, evolve over time in materials. You input existing microstructure simulation data, and it outputs a model that can simulate future grain growth patterns. This is useful for researchers and engineers working on designing new materials or improving existing ones.
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Use this if you need to quickly and interpretably simulate or emulate the process of microstructural grain growth in materials, without needing extensive computational physics knowledge.
Not ideal if you require highly detailed, atomic-level simulations of material behavior beyond microstructural grain growth or if you need a general-purpose materials simulation tool.
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Last pushed
Apr 01, 2025
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