基于TabPFN的零样本参数化工程设计生成
TabPFN for Zero-shot Parametric Engineering Design Generation
AI总结：
基于TabPFN的零样本生成方法，通过少量参考样本实现高效参数化工程设计生成，减少计算和数据需求，提升设计灵活性和实用性。
摘要：
深度生成模型在工程设计中往往需要大量的计算成本、大规模训练数据集以及在设计要求或数据集变化时进行广泛的重新训练，这限制了它们在实际工程设计流程中的应用。在本工作中，我们提出了一种基于TabPFN的零样本生成框架，用于参数化工程设计生成，仅需少量参考样本即可实现条件设计生成，而无需任何任务特定模型的训练或微调。所提出的方法按目标性能指标依次生成设计参数，提供了一种灵活的替代传统生成模型的方法。所提出方法在三个工程设计数据集上进行了评估，即船体设计、BlendedNet飞机和UIUC翼型。实验结果表明，所提出的方法在高度结构化的参数化设计空间中实现了有竞争力的多样性，对几何生成的采样变化、分辨率和参数维度变化具有鲁棒性，并在生成船体设计的性能上实现了低性能误差（例如，生成船体设计的性能误差低于2%）。与基于扩散的生成模型相比，所提出框架显著减少了计算开销和数据需求，同时保持了可靠的生成性能。这些结果突显了零样本、数据高效的生成作为实际和高效工程设计工具的潜力，能够实现快速部署、灵活适应新设计设置以及易于集成到实际工程流程中。
英文摘要：
Deep generative models for engineering design often require substantial computational cost, large training datasets, and extensive retraining when design requirements or datasets change, limiting their applicability in real-world engineering design workflow. In this work, we propose a zero-shot generation framework for parametric engineering design based on TabPFN, enabling conditional design generation using only a limited number of reference samples and without any task-specific model training or fine-tuning. The proposed method generates design parameters sequentially conditioned on target performance indicators, providing a flexible alternative to conventional generative models. The effectiveness of the proposed approach is evaluated on three engineering design datasets, i.e., ship hull design, BlendedNet aircraft, and UIUC airfoil. Experimental results demonstrate that the proposed method achieves competitive diversity across highly structured parametric design spaces, remains robust to variations in sampling, resolution and parameter dimensionality of geometry generation, and achieves a low performance error (e.g., less than 2% in generated ship hull designs' performance). Compared with diffusion-based generative models, the proposed framework significantly reduces computational overhead and data requirements while preserving reliable generation performance. These results highlight the potential of zero-shot, data-efficient generation as a practical and efficient tool for engineering design, enabling rapid deployment, flexible adaptation to new design settings, and ease of integration into real-world engineering workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
