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Bayesian optimization as a flexible and efficient design framework for sustainable process systems
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Bayesian optimization as a flexible and efficient design framework for sustainable process systems
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Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and beyond. In this paper, we provide an overview of recent developments, challenges, and opportunities in BO for design of next-generation process systems. After describing several motivating applications, we discuss how advanced BO methods have been developed to more efficiently tackle important problems in these applications. We conclude the paper with a summary of challenges and opportunities related to improving the quality of the probabilistic model, the choice of internal optimization procedure used to select the next sample point, and the exploitation of problem structure to improve sample efficiency.
Forward citations
Cited by 2 Pith papers
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EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
EARL-BO uses RL with an Attention-DeepSets encoder and end-to-end on-policy multi-task fine-tuning to approximate near-optimal multi-step lookahead policies for high-dimensional black-box optimization.
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Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Introduces PK-MIQP, a piecewise-linear kernel approximation that converts Gaussian process acquisition function optimization into a solvable MIQP for any stationary or dot-product kernel, with regret bounds and tests ...
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