Evaluating smart sampling for constructing multidimensional surrogate models
- Extensive numerical evaluation of smart sampling algorithm (SSA) is performed using a diverse test bed of analytical functions.
- Robustness of SSA is examined against Sobol sampling over the wide ranges of dimensions and domain sizes.
- Numerical comparison of SSA with existing adaptive approaches is illustrated.
- SSA is employed for three process systems engineering case studies to demonstrate its practical applicability.
In this article, we extensively evaluate the smart sampling algorithm (SSA) developed by Garud 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.
- This paper draws from preprint 185: Evaluating Smart Sampling for Constructing Multidimensional Surrogate Models
- Access the article at the publisher: DOI: 10.1016/j.compchemeng.2017.09.016