This is the code for paper "A deep learning-based crystal plasticity finite element model"
The basic requirement for using the files is a Python 3.6.3 environment with PyTorch 2.3.0
- MLtrain.py is the code for model training.
- Cp-ML-PowerLaw-func.py is the main code file for the whole CP-AI framework.
- Others are related functions of the CP-AI framework.
If you want to predict a stress-strain curve using this framework, please change the parameters in input file py.15, py_init.15, texture.15 and texture_init.15 (two init files include parameters for initial CP steps, the other two files include parameter for ML models). Then, run CP-ML-PowerLaw-func.py. The output is stress-strain data saved in file SS.txt.
The code was developed by Yuwei Mao from the CUCIS group at the Electrical and Computer Engineering Department at Northwestern University.
The research code shared in this repository is shared without any support or guarantee on its quality. However, please do raise an issue if you find anything wrong and I will try my best to address it.
email: [email protected]
Copyright (C) 2023, Northwestern University.
See COPYRIGHT notice in top-level directory.
This work is supported in part by the following grants: National Institute of Standards and Technology (NIST) award 70NANB19H005; Department of Energy (DOE) award DE-SC0021399; Na- tional Science Foundation (NSF) awards CMMI-2053929, OAC-2331329; and Northwestern Center for Nanocombinatorics.