Higher-order factorization machine for accurate surrogate modeling in material design.

ABSTRACT: Efficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to efficiently navigate large, high-dimensional design spaces. Among surrogate models, 2^nd-order factorization machine (FM) models are widely employed as the surrogate model in active learning algorithms due to their balance between simplicity and effectiveness. However, their quadratic nature limits their capacity to capture complex, higher-order interactions among variables, often leading to suboptimal solutions. To overcome this limitation, we propose an active learning scheme integrating a 3^rd-order FM model, capable of m

SUBMITTER: Hwang S 

PROVIDER: S-EPMC12511310 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature