Recent advances in artificial intelligence (AI) have impacted various fields, including mechanical engineering. However, the development of diverse, high-quality datasets for structural analysis remains a challenge. Traditional datasets, like the jet engine bracket dataset, are limited by small sample sizes, hindering the creation of robust surrogate models. This study introduces the DeepJEB dataset, generated through deep generative models and automated simulation pipelines, to address these limitations. DeepJEB offers comprehensive 3D geometries and corresponding structural analysis data. Key experiments validated its effectiveness, showing significant improvements in surrogate model performance. Models trained on DeepJEB achieved up to a 23% increase in the coefficient of determination and over a 70% reduction in mean absolute percentage error (MAPE) compared to those trained on traditional datasets. These results underscore the superior generalization capabilities of DeepJEB. By supporting advanced modeling techniques, such as graph neural networks (GNNs) and convolutional neural networks (CNNs), DeepJEB enables more accurate predictions in structural performance. The DeepJEB dataset is publicly accessible at: https://www.narnia.ai/dataset.

翻译：人工智能（AI）的最新进展已影响包括机械工程在内的多个领域。然而，用于结构分析的多样化、高质量数据集的开发仍面临挑战。传统数据集（如喷气发动机支架数据集）受限于样本量小，阻碍了鲁棒代理模型的构建。本研究引入DeepJEB数据集，该数据集通过深度生成模型和自动化仿真流程生成，以应对这些局限。DeepJEB提供全面的3D几何结构及相应的结构分析数据。关键实验验证了其有效性，显示代理模型性能得到显著提升。与传统数据集训练的模型相比，在DeepJEB上训练的模型决定系数最高提升23%，平均绝对百分比误差（MAPE）降低超过70%。这些结果凸显了DeepJEB卓越的泛化能力。通过支持图神经网络（GNNs）和卷积神经网络（CNNs）等先进建模技术，DeepJEB能够实现更精确的结构性能预测。DeepJEB数据集公开访问地址为：https://www.narnia.ai/dataset。