Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation
This work addresses trustworthiness issues in aircraft system simulations for engineers and researchers, representing an incremental improvement by integrating physics knowledge into adversarial methods.
The paper tackles the problem of ensuring physics consistency in deep learning models for aircraft system simulation by introducing a physics-guided adversarial machine learning approach, which successfully exposes and reduces physical inconsistencies in two aircraft system performance models.
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.