Neural Networks for Topology Optimization

This is the code for the paper I. Sosnovik, I. Oseledets "Neural Networks for Topology Optimization". [link][pdf]

In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce the convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance.

Preparations

We use TOP dataset to train the model.

In order to work with dataset easier, we aggregate the files into one .h5 dataset.

python prepare_data.py --source SOURCE_FOLDER --dataset-path WHERE_TO_SAVE

Training

You can train the model with script training.py. Here is the list of arguments:

• --dataset-path - path to .h5 dataset
• --input-size - size of the input tensor. Default is 40
• --batch-size - size of a minibatch. Default is 64
• --epochs - number of training epochs. Default is 30
• --vol-coeff - volume constraint coefficient in total loss. Default is 1.0
• --iter-sampler - iteration sampler. Either uniform or poisson_<LAM>, where LAM is lambda in Poisson distribution. Default is uniform
• --summary-prefix - root folder to save the summary
• --save-prefix - root folder to save the model

Just type bash experiments.sh to run default experiments.

Notebook results.ipynb demonstrates how to reproduce some of the results from the paper.

Citation

If you use this code for your research, please cite

@article{sosnovik2017neural,
  title={Neural networks for topology optimization},
  author={Sosnovik, Ivan and Oseledets, Ivan},
  journal={arXiv preprint arXiv:1709.09578},
  year={2017}
}