In this experience report, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations. We are interested in optimizing the design of structural components, where the shape is described by a set of parameters. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two selection strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our selection strategies in accelerating design optimization. We believe that the introduced method is easily transferable to other self-optimization problems.

翻译：在本经验报告中，我们将深度主动学习应用于设计优化领域，以减少计算成本高昂的数值模拟次数。我们致力于优化结构组件的设计，其形状由一组参数描述。若能基于这些参数预测性能，并仅对具有潜力的候选方案进行仿真，则可大幅节省计算资源。针对多目标设计优化问题，我们提出了两种自优化选择策略以降低计算成本。所提出的方法提供了一种直观易用的途径，相比随机采样具有显著改进，且无需进行不确定性估计。我们在流体力学领域的大规模数据集上评估了这些策略，并引入了两个新的评估指标来衡量模型性能。评估结果凸显了我们的选择策略在加速设计优化方面的有效性。我们相信，所提出的方法可轻松迁移至其他自优化问题。