  Surrogate Neural Networks are nowadays routinely used in industry as
substitutes for computationally demanding engineering simulations (e.g., in
structural analysis). They allow to generate faster predictions and thus
analyses in industrial applications e.g., during a product design, testing or
monitoring phases. Due to their performance and time-efficiency, these
surrogate models are now being developed for use in safety-critical
applications. Neural network verification and in particular the assessment of
their robustness (e.g., to perturbations) is the next critical step to allow
their inclusion in real-life applications and certification. We assess the
applicability and scalability of empirical and formal methods in the context of
aircraft predictive maintenance for surrogate neural networks designed to
predict the stress sustained by an aircraft part from external loads. The case
study covers a high-dimensional input and output space and the verification
process thus accommodates multi-objective constraints. We explore the
complementarity of verification methods in assessing the local stability
property of such surrogate models to input noise. We showcase the effectiveness
of sequentially combining methods in one verification 'pipeline' and
demonstrate the subsequent gain in runtime required to assess the targeted
property.
