Active Learning for Gaussian Process Considering Uncertainties With Application to Shape Control of Composite Fuselage
Xiaowei Yue, Yuchen Wen, Jeffrey H. Hunt, Jianjun Shi
Abstract
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.