In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This work presents a novel approach, physics-guided adversarial machine learning (ML), that improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously-uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both models and in improving their propensity to be consistent with physics domain knowledge.
翻译:在飞机系统性能评估方面,深层学习技术使得能够从实验测量中迅速推断出模型,其系统知识比以物理为基础的模型通常要求的要少,然而,这种低廉模型的开发也带来了模型可靠性方面的新挑战,这项工作提出了一种新颖的方法,即物理学引导的对抗机器学习(ML),提高了人们对模型物理学一致性的信心。该方法首先进行了物理引导的对立测试阶段,以寻找测试输入,揭示行为系统不一致之处,同时仍然处于可预见的操作条件范围之内。然后,该方法进行物理知情的对抗性培训,通过迭接式减少先前未覆盖的反射镜的意外产出偏差,向模型传授与系统有关的物理知识领域。对两个飞行器系统性能模型进行的经验性评估表明,我们的对抗性ML方法在暴露两种模型的物理不一致性并提高其与物理领域知识的一致性方面是有效的。