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The repository for the Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME) framework for learning and emulating microstructure grain growth.
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EAGG-UF/PRIMME
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============================================================================================= Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME) ============================================================================================= DESCRIPTION: Physics-Regularized Interpretable Machine Learning Microstructure Evolution (PRIMME) This code can be used to train and validate PRIMME neural network models for simulating isotropic microstructural grain growth CONTRIBUTORS: Weishi Yan (1), Joel Harley (1), Joseph Melville (1), Kristien Everett (1), Lin Yang (2) AFFILIATIONS: 1. University of Florida, SmartDATA Lab, Department of Electrical and Computer Engineering 2. University of Florida, Tonks Research Group, Department of Material Science and Engineering FUNDING SPONSORS: U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award \#DE-SC0020384 U.S. Department of Defence through a Science, Mathematics, and Research for Transformation (SMART) scholarship REQUIRMENTS: numpy scipy keras tensorflow torch tqdm h5py unfoldNd pynvml matplotlib imageio FOLDER/FILE DESCRIPTIONS: Top level folders: SPPARKS - Reference files to run SPPARKS simulations PRIMME - Actual PRIMME code "PRIMME" folder: cfg - Keras reference files spparks_files - See "Getting_Started.txt" for help getting SPPARKS functioning on the lambda server functions - All of the functions used to create initial conditions, run SPPARKS and PRIMME, and calculate statistics PRIMME - A class that contains the PRIMME model and some helper functions run - References 'functions' to run and evaluate SPPARKS and PRIMME simulations "functions" file (sections): Script - Set up folders and GPU General - File management functions Create initial conditions - See "voronoi2image" first Run and read SPPARKS - See "run_spparks" first Find misorientations - See "find_misorientation" first Statistical functions - See "compute_grain_stats" first Run PRIMME - See "run_primme" first Other notes: -The use of GPU 0 (or CPU is GPU 0 is not available) is hard coded in two places, at the beginning of both the "PRIMME" and "functions" files -The output of 'run.py' is the images of a circle grain PRIMME simulation.
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The repository for the Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME) framework for learning and emulating microstructure grain growth.
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