标题： 基于贝叶斯优化的轻量且精确的神经网络在气动性能预测中的应用 显示英文标题

标题： Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction

摘要： 确保预测模型的高精度和高效性在航空航天工业中至关重要，尤其是在多学科设计和优化过程的背景下。 这些过程通常需要对复杂的目标函数进行大量评估，这可能计算成本高昂且耗时。 为了构建高效且准确的预测模型，我们提出了一种新方法，利用贝叶斯优化（BO）来优化轻量且准确的神经网络（NN）的超参数，用于气动性能预测。 为了清晰地描述设计变量之间的相互作用，在BO公式中使用了分层和分类核。 我们通过两个全面的案例研究展示了我们方法的效率，其中优化后的NN在准确性和参数效率方面显著优于基线模型和其他公开可用的NN。 对于阻力系数预测任务，我们优化模型的平均绝对百分比误差（MAPE）从0.1433%降至0.0163%，这比基线模型提高了近一个数量级。 此外，我们的模型在基准飞机自我噪声预测问题上的MAPE为0.82%，显著优于现有模型（它们的MAPE值约为2到3%），同时所需的计算资源更少。 结果表明，我们的框架在大规模多学科设计优化问题中提升神经网络的可扩展性和性能具有潜力，为航空航天工业提供了一个有前景的解决方案。 显示英文摘要

摘要： Ensuring high accuracy and efficiency of predictive models is paramount in the aerospace industry, particularly in the context of multidisciplinary design and optimization processes. These processes often require numerous evaluations of complex objective functions, which can be computationally expensive and time-consuming. To build efficient and accurate predictive models, we propose a new approach that leverages Bayesian Optimization (BO) to optimize the hyper-parameters of a lightweight and accurate Neural Network (NN) for aerodynamic performance prediction. To clearly describe the interplay between design variables, hierarchical and categorical kernels are used in the BO formulation. We demonstrate the efficiency of our approach through two comprehensive case studies, where the optimized NN significantly outperforms baseline models and other publicly available NNs in terms of accuracy and parameter efficiency. For the drag coefficient prediction task, the Mean Absolute Percentage Error (MAPE) of our optimized model drops from 0.1433\% to 0.0163\%, which is nearly an order of magnitude improvement over the baseline model. Additionally, our model achieves a MAPE of 0.82\% on a benchmark aircraft self-noise prediction problem, significantly outperforming existing models (where their MAPE values are around 2 to 3\%) while requiring less computational resources. The results highlight the potential of our framework to enhance the scalability and performance of NNs in large-scale MDO problems, offering a promising solution for the aerospace industry.