Partitioned Active Learning for Heterogeneous Systems

TL;DR

This paper introduces a partitioned active learning approach for heterogeneous systems, combining global and local search strategies to improve surrogate modeling efficiency and accuracy in complex engineering applications.

Contribution

It proposes a novel partitioned active learning method with Cholesky update remedies, enhancing efficiency and accuracy for heterogeneous system modeling.

Findings

Outperforms benchmark methods in prediction accuracy

Reduces computational complexity significantly

Effective in real-world aerospace and materials science case studies

Abstract

Active learning is a subfield of machine learning that focuses on improving the data collection efficiency of expensive-to-evaluate systems. Especially, active learning integrated surrogate modeling has shown remarkable performance in computationally demanding engineering systems. However, the existence of heterogeneity in underlying systems may adversely affect the performance of active learning. In order to improve the learning efficiency under this regime, we propose the partitioned active learning that seeks the most informative design points for partitioned Gaussian process modeling of heterogeneous systems. The proposed active learning consists of two systematic subsequent steps: the global searching scheme accelerates the exploration of active learning by investigating the most uncertain design space, and the local searching exploits the circumscribed information induced by the local GP. We also propose Cholesky update driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method is applied to numerical simulations and two real-world case studies about (i) the cost-efficient automatic fuselage shape control in aerospace manufacturing; and (ii) the optimal design of tribocorrosion-resistant alloys in materials science. The results show that our approach outperforms benchmark methods with respect to prediction accuracy and computational efficiency.