Related papers: DEBOSH: Deep Bayesian Shape Optimization

DEBOSH: Deep Bayesian Shape Optimization

URL: http://arxiv.org/abs/2109.13337v2
Date: Mon, 2 Oct 2023 17:04:17 GMT
Title: DEBOSH: Deep Bayesian Shape Optimization
Authors: Nikita Durasov, Artem Lukoyanov, Jonathan Donier, Pascal Fua
Abstract summary: We propose a novel uncertainty-based method tailored to shape optimization. It enables effective BO and increases the quality of the resulting shapes beyond that of state-of-the-art approaches.
Score: 48.80431740983095
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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. Unfortunately, this is harder to do when using neural networks because standard approaches to estimating their uncertainty can entail high computational loads and reduced model accuracy. Hence, we propose a novel uncertainty-based method tailored to shape optimization. It enables effective BO and increases the quality of the resulting shapes beyond that of state-of-the-art approaches.

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