Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring using fibre Bragg grating data in Hawk T1A aircraft
Read the original at arxiv.org→arXiv:2610.22436v1 Announce Type: new Abstract: The signal generation capabilities of a novel data augmentation framework are examined in aerospace structures. First, the framework learns class-conditional temporal...
Original headline: "Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft"