Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases. For many scientific and engineering problems involving geometrical design, it is desirable for the surrogate models to precisely describe the change in geometry and predict the consequences. In that context, we develop graph neural networks (GNNs) as fast surrogate models for physics simulation, which allow us to directly train the models on 2/3D geometry designs that are represented by an unstructured mesh or point cloud, without the need for any explicit or hand-crafted parameterization. We utilize an encoder-processor-decoder-type architecture which can flexibly make prediction at both node level and graph level. The performance of our proposed GNN-based surrogate model is demonstrated on 2 example applications: feature designs in the domain of additive engineering and airfoil design in the domain of aerodynamics. The models show good accuracy in their predictions on a separate set of test geometries after training, with almost instant prediction speeds, as compared to O(hour) for the high-fidelity simulations required otherwise.

翻译：计算智能（CI）技术在替代昂贵物理仿真方面展现出巨大潜力，能够实现快速预测，尽管在某些情况下会牺牲精度。对于涉及几何设计的众多科学与工程问题，代理模型需要精确描述几何变化并预测其结果。为此，我们开发了基于图神经网络（GNN）的快速物理仿真代理模型，该模型可直接对以非结构化网格或点云表示的二维/三维几何设计进行训练，无需任何显式或手工参数化处理。我们采用编码器-处理器-解码器架构，可灵活地在节点级别和图级别进行预测。所提出的GNN代理模型性能在以下两个应用实例中得到验证：增材工程领域的特征设计以及空气动力学领域的翼型设计。经训练后，模型在独立测试几何数据集上展现出良好的预测精度，且预测速度近乎瞬时，而实现同等精度所需的高保真仿真通常需要数小时计算时间。