High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft
This work addresses efficiency in multidisciplinary design optimization for aerospace engineering, representing an incremental improvement in metamodeling techniques.
The paper tackles the challenge of high hyperparameter counts in mixed-categorical Gaussian processes by introducing a dimension reduction algorithm based on partial least squares regression, achieving a 439-kilogram reduction in fuel consumption for a green aircraft optimization.
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.