Surrogate-based optimization with adaptive sampling for microfluidic concentration gradient generator design

This paper presents a surrogate-based optimization (SBO) method with adaptive sampling for designing microfluidic concentration gradient generators (μCGGs) to meet prescribed concentration gradients (CGs). An efficient physics-based component model (PBCM) is used to generate data for Kriging-based s...

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Detalles Bibliográficos
Autores principales: Yang, Haizhou, Hong, Seong Hyeon, ZhG, Rei, Wang, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051574/
https://www.ncbi.nlm.nih.gov/pubmed/35493014
http://dx.doi.org/10.1039/d0ra01586e

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051574/
https://www.ncbi.nlm.nih.gov/pubmed/35493014
http://dx.doi.org/10.1039/d0ra01586e

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