DEBOSH: Deep Bayesian Shape Optimization

Understand

Graph Neural Networks (GNNs) can predict the performance of an industrial design quickly and accurately and be used to optimize its shape effectively.

• However, to fully explore the shape space, one must often consider shapes deviating significantly from the training set.
• For these, GNN predictions become unreliable, something that is often ignored.
• For optimization techniques relying on Gaussian Processes, Bayesian Optimization (BO) addresses this issue by exploiting their ability to assess their own accuracy.