Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation. (arXiv:2209.03431v1 [cs.LG])

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 …

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