发表机构
NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究比较了三种不确定性量化方法在几何条件神经CAE代理模型上的表现，发现各方法在不同数据集上性能各异，建议根据下游决策选择UQ方法和评估指标。
AI 中文摘要
神经代理模型可以大幅加速计算机辅助工程（CAE）工作流，但其在设计中的使用需要不确定性估计，这些估计必须在不同几何形状、空间预测场和工程关注量中保持有意义。我们研究了当既有的不确定性量化（UQ）方法被适配到几何条件神经代理模型时，它们的行为表现。我们比较了一种闭式方法和两种基于采样的方法——基于高斯过程（GP）的方法、具体蒙特卡洛（MC）dropout和深度集成——并在三个大型、工业相关的CAE数据集上（涉及外部空气动力学和碰撞动力学）对它们进行评估。我们检验了预测不确定性是否具有可信的量级，能否识别出预测误差较大的位置，是否对不熟悉的输入做出响应，以及是否对派生的工程关注量保持信息量。在DrivAerStar数据集上，三种方法均被比较，每种方法通常将更高的不确定性分配给预测误差较大的位置，并且基于验证集的重新缩放使得区间覆盖率在不相交的分布内测试集上接近名义水平。在AirFRANS和汽车碰撞上的结果也显示出有用的误差排序和区间估计，但方法的相对性能随数据集和评估标准而变化。因此，UQ方法和评估指标应根据预期的下游CAE决策来选择。
英文摘要
Neural surrogates can substantially accelerate computer-aided engineering (CAE) workflows, but their use in design requires uncertainty estimates that remain meaningful across varying geometries, spatial prediction fields, and engineering quantities of interest. We investigate how established uncertainty quantification (UQ) approaches behave when adapted to geometry-conditioned neural surrogates. We compare one closed-form and two sampling-based approaches-a Gaussian process (GP)-based method, concrete Monte Carlo (MC) dropout, and deep ensembles-and evaluate them on three large, industry-relevant CAE datasets for external aerodynamics and crash dynamics. We examine whether predicted uncertainties have credible magnitudes, identify locations with larger prediction errors, respond to unfamiliar inputs, and remain informative for derived engineering quantities. On the DrivAerStar dataset, where all three methods are compared, each generally assigns higher uncertainty to locations with larger prediction errors, and validation-based rescaling brings interval coverage close to nominal on a disjoint in-distribution test set. Results on AirFRANS and automotive crash also show useful error ranking and interval estimates, but the relative performance of the methods changes with the dataset and evaluation criterion. UQ methods and evaluation metrics should therefore be selected based on the intended downstream CAE decision.
