Transfer Learning in Bayesian Optimization for Aircraft Design

2026-03-30

AI Summary

This work proposes a novel framework integrating transfer learning with constrained Bayesian optimization to address key challenges in aircraft design, including cold-start conditions, heterogeneous variables, and complex constraints. By constructing an ensemble of surrogate models driven by metadata, and incorporating partial least squares dimensionality reduction alongside tailored strategies for handling heterogeneous variables, the approach significantly enhances prediction accuracy for both the objective function and constraint evaluations. The method accelerates convergence notably during early optimization stages while maintaining a favorable balance between modeling efficiency and optimization performance, thereby offering an effective solution pathway for high-dimensional, heterogeneous engineering design problems.

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.

Problem

• cold start
• heterogeneous design variables
• constraint heterogeneity
• aircraft design optimization
• Bayesian optimization

Innovation

• Transfer Learning
• Bayesian Optimization
• Surrogate Model Ensemble
• Partial Least Squares
• Constrained Optimization