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Deep De-Homogenization: pretrained models

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posted on 16.11.2021, 08:22 authored by Niels AageNiels Aage, Ole SigmundOle Sigmund, Martin Ohrt Elingaard, Jakob Andreas BærentzenJakob Andreas Bærentzen
Data related to the pretrained models used in:

Elingaard, M. O., Aage, N., Bærentzen, J. A., & Sigmund, O. (2022). De-homogenization using convolutional neural networks. Computer Methods in Applied Mechanics and Engineering, 388, 114197. https://doi.org/10.1016/j.cma.2021.114197

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 step2. For completion the weights used to initialize the training for the second step of the algorithm have also been included and are denoted step1. Files are saved in the .pth format, as recommended by PyTorch, and can be loaded using torch.load() or model.load​_state_dict(), see https://pytorch.org/tutorials/beginner/saving_loading_models.html for more information.


InnoTop Villum Investigator project


Related publications (DOI or link to DTU Orbit, DTU Findit)