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In the machine learning domain, active learning is an iterative dataselection algorithm for maximizing information acquisition and improving modelperformance with limited training samples. It is very useful, especially forthe industrial applications where training samples are expensive,time-consuming, or difficult to obtain. Existing methods mainly focus on activelearning for classification, and a few methods are designed for regression suchas linear regression or Gaussian process. Uncertainties from measurement errorsand intrinsic input noise inevitably exist in the experimental data, whichfurther affects the modeling performance. The existing active learning methodsdo not incorporate these uncertainties for Gaussian process. In this paper, wepropose two new active learning algorithms for the Gaussian process withuncertainties, which are variance-based weighted active learning algorithm andD-optimal weighted active learning algorithm. Through numerical study, we showthat the proposed approach can incorporate the impact from uncertainties, andrealize better prediction performance. This approach has been applied toimproving the predictive modeling for automatic shape control of compositefuselage.

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