Optimal Parameters Design for Manufacturability under Unknown Feasibility Constraints-Reference-Cited by-同舟云学术

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Optimal Parameters Design for Manufacturability under Unknown Feasibility Constraints

Published:2023-08-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)
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Author:

Li Guoyan¹,Jin Xiaoning¹,Wang Yujia²,Kar Swastik³

Affiliation:

1. Northeastern University,Department of Mechanical and Industrial Engineering,Boston,Massachusetts,USA,02115

2. Northeastern University,Department of Chemical Engineering,Boston,Massachusetts,USA,02115

3. Northeastern University,Department of Physics,Boston,Massachusetts,USA,02115

Funder

National Science Foundation

Northeastern University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10260321/10260291/10260375.pdf?arnumber=10260375

Reference18 articles.

1. Bayesian optimization with unknown constraints;gelbart;ArXiv Preprint,2014

2. Learning with local and global consistency;zhou;Advances in neural information processing systems,2003

3. Graph-based semi-supervised learning: A review

4. pBO-2GP-3B: A batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics

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