> Summary

The objective of this thesis is to optimize the structural performance of shell structures by exploring different topology options through an AI framework. The following steps are taken:

1. A new dataset is generated, consisting of several mesh tessellations created through a generative workflow.

2. Next, a Variational Autoencoder (VAE) is trained using this data. To train the VAE, two methods were tested: 

   1. The first method involves training the VAE using the coordinates of vertices as training data, but this was unsuccessful. 

   2. The second attempt employs an adjacency matrix as the training data. This approach proved to be more successful.

      1. A third attempt used only a quarter of the adjacency matrix so that the input size would be smaller.

      2. A  fourth attempt used a flattened part of the adjacency matrix, to minimize the size of the samples. This and this method proved to be faster. 

3. After training the VAE, a surrogate model was also trained to predict a performance score of decoded designs.

4. A design is selected and encoded.

5. The gradient of the design’s performance score with respect to its encoded vector is obtained, and a Gradient Descent Optimizer runs to optimize the initial design.

LIMITATIONS: The dataset was created using specific mesh boundaries and limited pattern exploration. The Variational Autoencoder (VAE) was the exclusive generative model used, and the optimization process focused solely on structural performance.

SPECIAL NOTE: Code parts can be found in appendix of the thesis paper.