一句话：提出基于平滑性的 OOD 检测方法 SmOOD，与 FNN 代理模型协同设计，提升飞机设计代理优化的可信度与效率。
飞机设计中，前馈神经网络代理模型虽能快速逼近高非线性性能映射，却难以泛化到分布外（OOD）样本，阻碍其在关键设计优化中的应用。为此，作者提出 SmOOD：一种基于平滑性的 OOD 检测方法，与优化后的 FNN 代理模型协同设计，形成模型相关的 OOD 指示器。不同于依赖不确定性的方法，SmOOD 利用高保真仿真的内在平滑性，通过揭示 OOD 样本的可疑敏感性来识别它们，避免对 OOD 的过度自信估计。仅高风险 OOD 输入被转发给高保真模型重评估，以低开销获得更准确结果。在三个飞机性能模型上评估表明，FNN 代理优于高斯过程；SmOOD 平均覆盖实际 OOD，并在混合代理优化中降低错误率、实现 58.36 倍加速。
Aircraft industry is constantly striving for more efficient design optimization methods in terms of human efforts, computation time, and resources consumption. Hybrid surrogate optimization maintains high results quality while providing rapid design assessments when both the surrogate model and the switch mechanism for eventually transitioning to the HF model are calibrated properly. Feedforward neural networks (FNNs) can capture highly nonlinear input-output mappings, yielding efficient surrogates for aircraft performance factors. However, FNNs often fail to generalize over the out-of-distribution (OOD) samples, which hinders their adoption in critical aircraft design optimization. Through SmOOD, our smoothness-based out-of-distribution detection approach, we propose to codesign a model-dependent OOD indicator with the optimized FNN surrogate, to produce a trustworthy surrogate model with selective but credible predictions. Unlike conventional uncertainty-grounded methods, SmOOD exploits inherent smoothness properties of the HF simulations to effectively expose OODs through revealing their suspicious sensitivities, thereby avoiding over-confident uncertainty estimates on OOD samples. By using SmOOD, only high-risk OOD inputs are forwarded to the HF model for re-evaluation, leading to more accurate results at a low overhead cost. Three aircraft performance models are investigated. Results show that FNN-based surrogates outperform their Gaussian Process counterparts in terms of predictive performance. Moreover, SmOOD does cover averagely of actual OODs on all the study cases. When SmOOD plus FNN surrogates are deployed in hybrid surrogate optimization settings, they result in a decrease error rate of and a computational speed up rate of 58.36 ×, respectively.
@inproceedings{paperbot7151,
title = {SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design},
author = {Houssem Ben Braiek and Ali Tfaily and Foutse Khomh and Thomas Reid and Ciro Guida},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556936}
}