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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Royal Society of Chemistry
2020
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| Materias: | |
| 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 |