发表机构
Toyota Central R&D Labs., Inc.; National Institute of Advanced Industrial Science and Technology (AIST)（丰田中央研发研究所; 产业技术综合研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对汽车轮辋程序设计，提出经筛选样本结合监督功能对齐的潜在空间学习方法，提升了设计建议的可行性与形状相似度，证明面向用户的表征是工程设计交互的核心。
AI 中文摘要
依赖基于偏好优化的智能设计界面，其价值在于所生成的建议既对用户有意义，又符合目标领域的可行性要求。程序模型能提供紧凑且可编辑的设计空间，但其原生参数存在纠缠问题，还会生成大量无效输出，导致人在回路优化器浪费比较资源。我们提出一种面向程序模型的交互导向表征学习流水线，并以汽车轮辋设计为研究对象展开验证。该方法首先通过几何规则和有限元分析筛选程序生成的样本，再从筛选后的子集中学习降维潜在空间；我们进一步引入监督功能对齐机制，为刚度、与强度相关的应力响应或权重预留选定的潜在维度，使搜索能偏向功能有意义的区域。模拟实验表明，经过筛选的降维可提升目标形状检索效果和建议的可行性率，而未经过筛选的降维则会降低这两项指标；额外模拟显示，沿学习到的功能维度约束搜索，能加快对目标功能属性的探索。针对40名参与者的对照研究进一步表明，与原始9维程序参数化相比，5维可行性感知空间能产生更高的形状相似度和更多可行建议。这些结果提示，对于工程设计中的智能用户界面，向用户暴露的表征是交互设计的核心部分，而非仅优化器的预处理步骤。
英文摘要
Intelligent design interfaces that rely on preference-based optimization are most useful when their suggestions are both meaningful to users and feasible within the target domain. Procedural models offer compact and editable design spaces, but their native parameters can be entangled and can generate many invalid outputs, causing human-in-the-loop optimizers to waste comparisons. We propose an interaction-oriented representation-learning pipeline for procedural models and study it in automotive wheel design. The method first screens procedurally generated samples using geometric rules and finite-element analysis, then learns a reduced latent space from the screened subset. We further introduce supervised functional alignment, which reserves selected latent dimensions for stiffness, strength-related stress response, or weight so that search can be biased toward functionally meaningful regions. Simulation experiments show that screened reduction improves target-shape retrieval and the feasibility rate of suggestions, whereas unscreened reduction degrades both. Additional simulations show that constraining search along learned functional dimensions accelerates exploration toward target functional properties. A controlled study with 40 participants further shows that a 5D feasibility-aware space yields higher shape similarity and more feasible suggestions than the original 9D procedural parameterization. These results suggest that, for intelligent user interfaces in engineering design, the representation exposed to the user is a central part of the interaction design, not merely a preprocessing step for the optimizer.
