Surrogate Neural Networks Local Stability for Aircraft Predictive
  Maintenance
Mélanie Ducoffe Guillaume Povéda Audrey Galametz Ryma Boumazouza Marion-Cécile Martin Julien Baris Derk Daverschot Eugene O'Higgins
Surrogate Neural Networks (NN) now routinely serve as substitutes for
computationally demanding simulations (e.g., finite element). They enable
faster analyses in industrial applications e.g., manufacturing processes,
performance assessment. The verification of surrogate models is a critical step
to assess their robustness under different scenarios. We explore the
combination of empirical and formal methods in one NN verification pipeline. We
showcase its efficiency on an industrial use case of aircraft predictive
maintenance. We assess the local stability of surrogate NN designed to predict
the stress sustained by an aircraft part from external loads. Our contribution
lies in the complete verification of the surrogate models that possess a
high-dimensional input and output space, thus accommodating multi-objective
constraints. We also demonstrate the pipeline effectiveness in substantially
decreasing the runtime needed to assess the targeted property.