Active Learning for Gaussian Process Considering Uncertainties With Application to Shape Control of Composite Fuselage

In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for industrial applications where training samples are expensive, time-consuming, or difficult to obtain. Existing methods mainly focus on active learning for classification, and a few methods are designed for regression, such as linear regression or Gaussian process (GP). Uncertainties from measurement errors and intrinsic input noise inevitably exist in the experimental data, which further affects the modeling performance. The existing active learning methods do not incorporate these uncertainties for GP. In this article, we propose two new active learning algorithms for the GP with uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. Through numerical study, we show that the proposed approach can incorporate the impact of uncertainties and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage. Note to Practitioners-This article was motivated by automatic shape control of composite fuselage. The main objective is to realize active learning for predictive analytics, which means maximizing information acquisition with limited experimental samples. This kind of need for active learning is very common in the industrial systems where it is expensive, time-consuming, or difficult to obtain experimental data. Existing approaches, from either a machine learning perspective or a statistics perspective, mainly focus on active learning for classification or regression models without incorporating impacts from intrinsic input uncertainties. However, intrinsic uncertainties widely exist in industrial systems. This article develops two active learning algorithms for the Gaussian process with uncertainties. The algorithms take variance-based information measure and Fisher information measure into consideration. The proposed algorithms can also be applied in other active learning scenarios, specifically for predictive models with multiple uncertainties.

Automated summary

This paper introduces two new active learning algorithms for Gaussian process with uncertainties and demonstrates their effectiveness through numerical study. These algorithms can improve prediction performance, especially in industrial systems where it is often difficult to obtain sufficient experimental data.