Deep De-Homogenization: pretrained models

<div>Data related to the pretrained models used in: <br></div><div><br></div><div>Elingaard, M. O., Aage, N., Bærentzen, J. A., & Sigmund, O. (2022). De-homogenization using convolutional neural networks. <i>Computer Methods in Applied Mechanics and Engineering</i>, <i>388</i>, 114197. https://doi.org/10.1016/j.cma.2021.114197</div><div><br></div><div>The directory contains two different models, one for a frequency of 10 pixels/period, and one for a frequency of 20 pixels/period. These have been denoted <i>step2</i>. For completion the weights used to initialize the training for the second step of the algorithm have also been included and are denoted <i>step1. </i>Files are saved in the .pth format, as recommended by PyTorch, and can be loaded using torch.load() or model.load​_state_dict(), see <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html" rel="noopener noreferrer" target="_blank">https://pytorch.org/tutorials/beginner/saving_loading_models.html</a> for more information.</div>