mlsanityData to 14 Oct 2026

Towards certification: A complete statistical validation pipeline for supervised learning in industry

arXiv 2411.02075 ↗First posted 4 Nov 2024cs.LG physics.data-ancited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 4 Nov 2024.

Builds on

18 of 51 references.

References · 18

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6 more
  1. 2023An effective theory of collective deep learningcited by 7
  2. 2022Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applicationscited by 6
  3. 2024Dynamical stability and chaos in artificial neural network trajectories along trainingcited by 6
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraintscited by 4
  2. 2026FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshescited by 3
  3. 2025Reliable Statistical Guarantees for Conformal Predictors with Small Datasetscited by 1
  4. 2026Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNscited by 0