Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation

TL;DR

This paper introduces a physics-guided adversarial machine learning approach that enhances the trustworthiness of aircraft system models by identifying and correcting physical inconsistencies through adversarial testing and training.

Contribution

It presents a novel physics-guided adversarial ML framework that detects and reduces physical inconsistencies in aircraft system models, improving their physics compliance.

Findings

Effectively exposes physical inconsistencies in aircraft models.

Improves model physics consistency through adversarial training.

Demonstrates success on two aircraft system models.

Abstract

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.