Evaluating smart sampling for constructing multidimensional surrogate models
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...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/90099 http://hdl.handle.net/10220/48382 |
| _version_ | 1811685636011196416 |
|---|---|
| author | Garud, Sushant S. Karimi, Iftekhar A. Brownbridge, George P.E. Kraft, Markus |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering Garud, Sushant S. Karimi, Iftekhar A. Brownbridge, George P.E. Kraft, Markus |
| author_sort | Garud, Sushant S. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:47:40Z |
| format | Journal Article |
| id | ntu-10356/90099 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:47:40Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/900992023-12-29T06:48:14Z Evaluating smart sampling for constructing multidimensional surrogate models Garud, Sushant S. Karimi, Iftekhar A. Brownbridge, George P.E. Kraft, Markus School of Chemical and Biomedical Engineering Adaptive Sampling Experimental Design DRNTU::Engineering::Chemical engineering 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. NRF (Natl Research Foundation, S’pore) Accepted version 2019-05-27T07:49:01Z 2019-12-06T17:40:38Z 2019-05-27T07:49:01Z 2019-12-06T17:40:38Z 2018 Journal Article Garud, S. S., Karimi, I. A., Brownbridge, G. P., & Kraft, M. (2018). Evaluating smart sampling for constructing multidimensional surrogate models. Computers & Chemical Engineering, 108, 276-288. doi:10.1016/j.compchemeng.2017.09.016 0098-1354 https://hdl.handle.net/10356/90099 http://hdl.handle.net/10220/48382 10.1016/j.compchemeng.2017.09.016 en Computers & Chemical Engineering © 2017 Elsevier Ltd.. All rights reserved. This paper was published in Computers & Chemical Engineering and is made available with permission of Elsevier Ltd. 45 p. application/pdf |
| spellingShingle | Adaptive Sampling Experimental Design DRNTU::Engineering::Chemical engineering Garud, Sushant S. Karimi, Iftekhar A. Brownbridge, George P.E. Kraft, Markus Evaluating smart sampling for constructing multidimensional surrogate models |
| title | Evaluating smart sampling for constructing multidimensional surrogate models |
| title_full | Evaluating smart sampling for constructing multidimensional surrogate models |
| title_fullStr | Evaluating smart sampling for constructing multidimensional surrogate models |
| title_full_unstemmed | Evaluating smart sampling for constructing multidimensional surrogate models |
| title_short | Evaluating smart sampling for constructing multidimensional surrogate models |
| title_sort | evaluating smart sampling for constructing multidimensional surrogate models |
| topic | Adaptive Sampling Experimental Design DRNTU::Engineering::Chemical engineering |
| url | https://hdl.handle.net/10356/90099 http://hdl.handle.net/10220/48382 |
| work_keys_str_mv | AT garudsushants evaluatingsmartsamplingforconstructingmultidimensionalsurrogatemodels AT karimiiftekhara evaluatingsmartsamplingforconstructingmultidimensionalsurrogatemodels AT brownbridgegeorgepe evaluatingsmartsamplingforconstructingmultidimensionalsurrogatemodels AT kraftmarkus evaluatingsmartsamplingforconstructingmultidimensionalsurrogatemodels |