用有限数据实现3D形状优化，提升设计效率与质量。
Three-dimensional Deep Shape Optimization with a Limited Dataset
- 结合位置编码与Lipschitz正则化，增强小样本下几何特征学习能力。
- 在轮毂、汽车等多类3D数据集上实现多目标优化，生成高质量设计。
- 适合数据稀缺场景下的工业设计自动化，尤其适用于机械结构优化。
生成模型因其创造新形状的能力受到广泛关注，但在机械设计中的应用受限于现有数据集规模小且多样性不足。本文提出一种面向小样本数据的深度学习形状优化框架，通过引入位置编码和Lipschitz正则化项，有效学习几何特征并保持有意义的潜在空间。大量实验表明，该方法在典型优化框架的局限性面前具有鲁棒性、泛化性和有效性。在轮毂、汽车等多类三维数据集上进行的多目标优化实验验证了该方法的实用性，即使在数据受限条件下也能生成可实际应用的高质量设计结果。
原文摘要 · Abstract (English)
Generative models have attracted considerable attention for their ability to produce novel shapes. However, their application in mechanical design remains constrained due to the limited size and variability of available datasets. This study proposes a deep learning-based optimization framework specifically tailored for shape optimization with limited datasets, leveraging positional encoding and a Lipschitz regularization term to robustly learn geometric characteristics and maintain a meaningful latent space. Through extensive experiments, the proposed approach demonstrates robustness, generalizability and effectiveness in addressing typical limitations of conventional optimization frameworks. The validity of the methodology is confirmed through multi-objective shape optimization experiments conducted on diverse three-dimensional datasets, including wheels and cars, highlighting the model's versatility in producing practical and high-quality design outcomes even under data-constrained conditions.
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