Towards certification: A complete statistical validation pipeline for supervised learning in industry
This work addresses the need for reliable certification of AI systems in safety-critical industries like aerospace, representing an incremental step in adapting existing methods to industrial scenarios.
The paper tackles the challenge of certifying supervised learning models for industrial use, particularly in aerospace, by proposing a ten-step validation pipeline that integrates deep learning, optimization, and statistical methods, and demonstrates its application in predicting stress-related failure modes in aerostructural design.
Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper aims to contribute to this paradigm of AI-based certification in the context of supervised learning, by outlining a complete validation pipeline that integrates deep learning, optimization and statistical methods. This pipeline is composed by a directed graphical model of ten steps. Each of these steps is addressed by a merging key concepts from different contributing disciplines (from machine learning or optimization to statistics) and adapting them to an industrial scenario, as well as by developing computationally efficient algorithmic solutions. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design: predicting the likelikood of different stress-related failure modes during different airflight maneuvers based on a (large) set of features characterising the aircraft internal loads and geometric parameters.