Composite Gaussian process models with an analytical profit function and a steady-state energy-balance residual are embedded in Bayesian optimization to improve economic performance and constraint satisfaction on a simulated multi-product chemical reactor.
Transfer learning for bayesian optimization: A survey.arXiv preprint arXiv:2302.05927
2 Pith papers cite this work, alongside 18 external citations. Polarity classification is still indexing.
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BayMOTH unifies meta-Bayesian optimization with a usefulness-based fallback to lookahead, demonstrating competitive results on function optimization tasks even under low task relatedness.
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Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge
Composite Gaussian process models with an analytical profit function and a steady-state energy-balance residual are embedded in Bayesian optimization to improve economic performance and constraint satisfaction on a simulated multi-product chemical reactor.
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BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH
BayMOTH unifies meta-Bayesian optimization with a usefulness-based fallback to lookahead, demonstrating competitive results on function optimization tasks even under low task relatedness.