High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft

TL;DR

This paper introduces a novel dimension reduction algorithm for mixed-categorical Gaussian Processes, enabling efficient Bayesian optimization in complex engineering problems like green aircraft design, leading to substantial fuel savings.

Contribution

It proposes a new dimension reduction method based on partial least squares regression for mixed-variable GPs, extending classical techniques to handle categorical inputs.

Findings

Effective reduction of hyperparameters in mixed-categorical GPs

Successful application to structural and multidisciplinary problems

Achieved 439 kg fuel savings in green aircraft optimization

Abstract

Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be used to build the mixed-categorical GP. Many of these approaches involve a high number of hyperparameters; in fact, the more general and precise the strategy used to build the GP, the greater the number of hyperparameters to estimate. This paper introduces an innovative dimension reduction algorithm that relies on partial least squares regression to reduce the number of hyperparameters used to build a mixed-variable GP. Our goal is to generalize classical dimension reduction techniques commonly used within GP (for continuous inputs) to handle mixed-categorical inputs. The good potential of the proposed method is demonstrated in both structural and multidisciplinary application contexts. The targeted applications include the analysis of a cantilever beam as well as the optimization of a green aircraft, resulting in a significant 439-kilogram reduction in fuel consumption during a single mission.