Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science.

ABSTRACT: Obtaining a fine approximation of a black-box function is important for understanding and evaluating innovative materials. Active learning aims to improve the approximation of black-box functions with fewer training data. In this study, we investigate whether active learning based on uncertainty sampling enables the efficient approximation of black-box functions in regression tasks using various material databases. In cases where the inputs are provided uniformly and defined in a relatively low-dimensional space, the liquidus surfaces of the ternary systems are the focus. The results show that uncertainty-based active learning can produce a better black-box function with higher prediction accuracy than that by random sampling. Furthermore, in cases in which the inputs are distributed discr

SUBMITTER: Koizumi A 

PROVIDER: S-EPMC11541781 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature