Stochastic sampling using moving least squares response surface approximations
This work discusses the simulation of samples from a target probability distribution which is related to the response of a system model that is computationally expensive to evaluate. Implementation of surrogate modeling, in particular moving least squares (MLS) response surface methodologies, is sug...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/100845 http://hdl.handle.net/10220/16888 |
| _version_ | 1826125297006673920 |
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| author | Taflanidis, Alexandros A. Cheung, Sai Hung |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Taflanidis, Alexandros A. Cheung, Sai Hung |
| author_sort | Taflanidis, Alexandros A. |
| collection | NTU |
| description | This work discusses the simulation of samples from a target probability distribution which is related to the response of a system model that is computationally expensive to evaluate. Implementation of surrogate modeling, in particular moving least squares (MLS) response surface methodologies, is suggested for efficient approximation of the model response for reduction of the computational burden associated with the stochastic sampling. For efficient selection of the MLS weights and improvement of the response surface approximation accuracy, a novel methodology is introduced, based on information about the sensitivity of the sampling process with respect to each of the model parameters. An approach based on the relative information entropy is suggested for this purpose, and direct evaluation from the samples available from the stochastic sampling is discussed. A novel measure is also introduced for evaluating the accuracy of the response surface approximation in terms relevant to the stochastic sampling task. |
| first_indexed | 2024-10-01T06:34:20Z |
| format | Journal Article |
| id | ntu-10356/100845 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:34:20Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1008452020-03-07T11:43:47Z Stochastic sampling using moving least squares response surface approximations Taflanidis, Alexandros A. Cheung, Sai Hung School of Civil and Environmental Engineering This work discusses the simulation of samples from a target probability distribution which is related to the response of a system model that is computationally expensive to evaluate. Implementation of surrogate modeling, in particular moving least squares (MLS) response surface methodologies, is suggested for efficient approximation of the model response for reduction of the computational burden associated with the stochastic sampling. For efficient selection of the MLS weights and improvement of the response surface approximation accuracy, a novel methodology is introduced, based on information about the sensitivity of the sampling process with respect to each of the model parameters. An approach based on the relative information entropy is suggested for this purpose, and direct evaluation from the samples available from the stochastic sampling is discussed. A novel measure is also introduced for evaluating the accuracy of the response surface approximation in terms relevant to the stochastic sampling task. 2013-10-25T02:47:26Z 2019-12-06T20:29:16Z 2013-10-25T02:47:26Z 2019-12-06T20:29:16Z 2011 2011 Journal Article Taflanidis, A. A., & Cheung, S. H. (2012). Stochastic sampling using moving least squares response surface approximations. Probabilistic engineering mechanics, 28, 216-224. 0266-8920 https://hdl.handle.net/10356/100845 http://hdl.handle.net/10220/16888 10.1016/j.probengmech.2011.07.003 en Probabilistic engineering mechanics © 2011 Elsevier Ltd. |
| spellingShingle | Taflanidis, Alexandros A. Cheung, Sai Hung Stochastic sampling using moving least squares response surface approximations |
| title | Stochastic sampling using moving least squares response surface approximations |
| title_full | Stochastic sampling using moving least squares response surface approximations |
| title_fullStr | Stochastic sampling using moving least squares response surface approximations |
| title_full_unstemmed | Stochastic sampling using moving least squares response surface approximations |
| title_short | Stochastic sampling using moving least squares response surface approximations |
| title_sort | stochastic sampling using moving least squares response surface approximations |
| url | https://hdl.handle.net/10356/100845 http://hdl.handle.net/10220/16888 |
| work_keys_str_mv | AT taflanidisalexandrosa stochasticsamplingusingmovingleastsquaresresponsesurfaceapproximations AT cheungsaihung stochasticsamplingusingmovingleastsquaresresponsesurfaceapproximations |