Compressing thousands of constraints into a low-dimensional latent space lets Bayesian optimization solve a 108D aeroelastic-tailoring problem with 1,786 black-box constraints, at the cost of slightly worse solution quality on a benchmark.
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High-Dimensional Bayesian Optimisation with Large-Scale Constraints via Latent Space Gaussian Processes
Compressing thousands of constraints into a low-dimensional latent space lets Bayesian optimization solve a 108D aeroelastic-tailoring problem with 1,786 black-box constraints, at the cost of slightly worse solution quality on a benchmark.