为工业监督学习提供完整的统计验证流程，确保AI模型可靠落地。
Towards certification: A complete statistical validation pipeline for supervised learning in industry
- 构建十步流程图，融合机器学习、优化与统计方法。
- 在飞机结构设计中成功预测多种工况下的应力失效概率。
- 适合航空航天等高安全领域需AI认证的场景。
机器学习与深度学习正逐步融入工业应用，但各行业进展不一。航空航天领域已制定神经网络技术在航空领域的设计保证与集成路线图。本文针对监督学习中的AI认证范式，提出一套完整的验证流程，整合深度学习、优化与统计方法。该流程以十步有向图形式呈现，每一步结合多学科核心思想（如机器学习、优化、统计），并针对工业场景进行适配，同时开发高效算法。通过一个真实的航空结构设计问题展示其应用：基于大量表征飞机内部载荷与几何参数的特征，预测不同飞行工况下各类应力相关失效模式的概率。
原文摘要 · Abstract (English)
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.
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