发表机构
School of Ocean and Civil Engineering, Shanghai Jiao Tong University; Eastern Institute of Technology; TenFong Technology Co., Ltd.; IM Motors Technology Co., Ltd.（上海交通大学海洋与土木工程学院; 东方理工学院; 十方科技有限公司; 集度汽车科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对工程形状优化中专家依赖和代理模型可靠性问题，提出知识约束形状优化框架，开发专家混合神经算子MoE-NO，通过不确定性估计策略改进阻力预测等，实验表明其在测试集和车辆形状优化上效果良好，降低了阻力系数。
AI 中文摘要
工程形状优化在依赖专家的问题设置和代理模型可靠性方面面临挑战。在实际空气动力学设计中，诸如可编辑区域等优化设置通常由经验丰富的工程师手动指定，而基于代理的优化对于异构几何数据库和分布外设计可能变得不可靠。为应对这些挑战，我们提出了一个知识约束形状优化框架，将基于知识的约束和用户意图转化为基于DFFD变形算子的可量化参数。我们还开发了专家混合神经算子（MoE-NO）来改进对异构空气动力学数据集的阻力预测和趋势一致性。基于MoE-NO编码器和马氏距离，引入了一种不确定性估计策略来检测分布外几何形状并选择性地触发物理求解器反馈以进行局部样本富集。在内部MPV、SUV和轿车数据集上的实验表明，MoE-NO在测试集上的平均绝对百分比误差为1.16%，趋势预测准确率为94.34%，优于最佳基线结果。车辆形状优化实验进一步使CFD验证的阻力系数降低了约4%至10%。
英文摘要
Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability. In practical aerodynamic design, optimization settings such as editable regions, deformation ranges, and design-preservation constraints are typically specified manually by experienced engineers, while surrogate-based optimization may become unreliable for heterogeneous geometry databases and out-of-distribution designs. To address these challenges, we propose a knowledge-constrained shape-optimization framework that translates knowledge-based constraints and user intent into quantifiable parameters of DFFD-based deformation operators, enabling engineering-aware and controllable constrained optimization. We further develop a Mixture-of-Experts Neural Operator (MoE-NO) to improve drag prediction and trend consistency over heterogeneous aerodynamic datasets. Based on the MoE-NO encoder and Mahalanobis distance, an uncertainty-estimation strategy is introduced to detect out-of-distribution geometries and selectively trigger physics-solver feedback for local sample enrichment. Experiments on in-house MPV, SUV, and Sedan datasets show that MoE-NO achieves a test-set MAPE of $1.16\%$ and a trend-prediction accuracy of $94.34\%$, outperforming the best baseline results of $1.52\%$ and $90.34\%$, respectively. Vehicle shape-optimization experiments further yield CFD-validated drag coefficient reductions of approximately $4\%$ to $10\%$.
