.png){kind=icon}
Return
Evaluating smart sampling for constructing multidimensional surrogate models
.png){kind=icon}
OA
AI
DOI：10.1016/j.compchemeng.2017.09.016.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, we extensively evaluate the smart sampling algorithm (SSA) developed by Galud et al. (2017a) for constructing multidimensional surrogate models. Our numerical evaluation shows that SSA outperforms Sobol sampling (QS) for polynomial and kriging surrogates on a diverse test bed of 13 functions. Furthermore, we compare the robustness of SSA against QS by evaluating them over ranges of domain dimensions and edge length/s. SSA shows consistently better performance than QS making it viable for a broad spectrum of applications. Besides this, we show that SSA performs very well compared to the existing adaptive techniques, especially for the high dimensional case. Finally, we demonstrate the practicality of SSA by employing it for three case studies. Overall, SSA is a promising approach for constructing multidimensional surrogates at significantly reduced computational cost. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Adaptive sampling
Experimental design
Surrogate model
Process flowsheeta
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSaveShareSave View more