Partitioned Active Learning for Heterogeneous Systems
This work addresses the challenge of active learning for heterogeneous systems in computationally demanding engineering applications, such as aerospace manufacturing and materials science, representing an incremental improvement over existing methods.
The paper tackles the problem of active learning in heterogeneous systems, which can reduce learning efficiency, by proposing partitioned active learning with global and local searching steps and numerical remedies, achieving improved prediction accuracy and computational efficiency in numerical simulations and real-world case studies.
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.