Transfer Learning in Bayesian Optimization for Aircraft Design

Abstract

The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \textit{cold start} problem by using source data to aid in the optimization of a target problem. We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework. We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints. We propose the use of a partial-least-squares dimension reduction algorithm to address design space heterogeneity, and a \textit{meta} data surrogate selection method to address constraint heterogeneity. Numerical benchmark problems and an aircraft conceptual design optimization problem are used to demonstrate the proposed methods. Results show significant improvement in convergence in early optimization iterations compared to standard Bayesian optimization, with improved prediction accuracy for both objective and constraint surrogate models.

Figures (11)

• Figure 1: Scaled top views of selected aircraft from the source problem's DOE highlighting the differences in wing and fuselage geometries.
• Figure 2: XDSM representation of the aircraft conceptual design MDA framework.
• Figure 3: Heterogeneous transfer learning categories (adapted from bao2023survey).
• Figure 4: Heterogeneous data-based transfer learning methods (adapted from bao2023survey).
• Figure 5: Heterogeneous design space in transfer learning using meta data.