Shape optimization is at the heart of many industrial applications, such as
aerodynamics, heat transfer, and structural analysis. It has recently been
shown that Graph Neural Networks (GNNs) can predict the performance of a shape
quickly and accurately and be used to optimize more effectively than
traditional techniques that rely on response-surfaces obtained by Kriging.
However, GNNs suffer from the fact that they do not evaluate their own
accuracy, which is something Bayesian Optimization methods require. Therefore,
estimating confidence in generated predictions is necessary to go beyond
straight deterministic optimization, which is less effective.
In this paper, we demonstrate that we can use Ensembles-based technique to
overcome this limitation and outperform the state-of-the-art. Our experiments
on diverse aerodynamics and structural analysis tasks prove that adding
uncertainty to shape optimization significantly improves the quality of
resulting shapes and reduces the time required for the optimization.

本论文证明了使用基于 Ensembles 的技术来评估 Graph Neural Networks 生成的预测的不确定性可以帮助超越传统的基于 Kriging 得到响应面的技术，并在各种气动和结构分析任务中提供更高质量的优化结果和更短的优化时间。