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This is a Quantitative Study study.

Autoencoder-Enhanced Joint Dimensionality Reduction for Constrained Bayesian Optimisation

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AERO-BO improves efficiency in optimising high-dimensional engineering problems with many constraints.

AERO-BO enhances bayesian optimisation by addressing the challenges of high-dimensional inputs and outputs.
The framework utilizes autoencoders for dimensionality reduction across both input and output spaces.
Gaussian processes serve as surrogate models, effectively linking the latent spaces during optimisation.
This approach can handle hundreds of design variables and thousands of constraints in engineering optimisations.

Maathuis et al. (2025) studied this question.

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