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arXiv Machine Learning By Alvaro Peiro, Marios Impraimakis

Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft

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The paper presents a data‑augmentation framework that uses conditional generative adversarial networks and Monte Carlo dropout to synthesize structural response signals with quantified uncertainty. The synthetic signals are rigorously compared to experimental data from a BAE Systems Hawk T1A aircraft, showing accurate capture of dominant dynamics and realistic statistical properties. The study evaluates whether the augmented data can improve supervised damage classification and demonstrates the framework’s potential as a digital twin for safety‑critical aerospace structural health monitoring.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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