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Lancaster University
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Learnable shape parameterizations unlock superior sample efficiency and design exploration in Bayesian optimization, outperforming hand-crafted approaches across diverse engineering domains.
State-preserving constraints in Bayesian optimization are transformed into an efficient, anisotropic ellipsoid in design space, enabling robust optimization of complex engineering systems.
