用两阶段方法从性能需求生成汽车引擎盖内板，兼顾结构强度与轻量化。
KPI-Conditioned Generative Design of Automotive Hood Inner Panels: A Two-Stage Retrieval-Generation Pipeline with Surrogate-Based Performance Estimation
- 先筛选可满足要求的拓扑结构，再生成具体几何形状
- 生成设计平均达标率超85%，但单个设计可靠性受限于模型误差
- 适合需要快速生成工业级结构方案的工程师使用
引擎盖内板需满足变形量、应力和质量三项指标。机器学习代理模型已使从几何到性能的正向预测变得快速常规，但反向设计——从性能要求生成几何——在拓扑结构离散分布而非连续参数化的工业部件中仍缺乏系统方法。本文提出两阶段管道：第一阶段通过可达性分析确定可满足给定需求向量的拓扑家族；第二阶段采用条件变分自编码器生成点云几何，并由神经算子代理模型评估其性能。整个流程仅依赖公开数据和免费算力，已部署为交互式工具。实验表明该方法有效，主要发现为：代理模型整体准确，但其误差与需区分的性能差异相当，限制了对单个生成设计的可信度判断。作者认为，代理误差与类内信号之比决定了此类管道能否成立。
原文摘要 · Abstract (English)
An inner hood panel must meet a deflection target, stay below a stress limit, and hit a mass target. Machine-learned surrogates have made the forward direction, geometry to performance, fast and routine. The inverse direction, producing geometry from a stated requirement, remains largely unaddressed for industrial parts whose design space is organized into discrete topology families rather than a continuous parameterization. This work presents a two-stage pipeline for that inverse problem. A reachability stage determines which topology families can satisfy a given requirement vector. A conditional variational autoencoder then generates point-cloud geometry within a selected family, and a neural-operator surrogate estimates the performance of each candidate. The pipeline is built entirely from public data and freely available compute, and is deployed as an interactive tool. The pipeline works, with qualifications that are reported as primary findings rather than caveats. The surrogate is accurate in aggregate, but its error is comparable to the performance differences it is asked to discriminate, which bounds what can be claimed for any individual generated design. That ratio of surrogate error to within-class signal is argued to be the quantity that determines whether a pipeline of this kind can work at all.
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