Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture

Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Eve...
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Autor*in:	Barkhori, Moien [verfasserIn] Shayanfar, Mohsen Ali Barkhordari, Mohammad Ali Bakhshpoori, Taha

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Reliability analysis Adaptive importance sampling Cross-entropy Kriging model Gaussian mixture

Anmerkung:	© Shiraz University 2018

Übergeordnetes Werk:	Enthalten in: Iranian journal of science and technology - Shiraz : Shiraz University, 2001, 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88
Übergeordnetes Werk:	volume:43 ; year:2018 ; number:Suppl 1 ; day:04 ; month:09 ; pages:81-88

Links:	Volltext

DOI / URN:	10.1007/s40996-018-0143-y

Katalog-ID:	SPR038045796

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520			\|a Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy.
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650		4	\|a Adaptive importance sampling \|7 (dpeaa)DE-He213
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650		4	\|a Kriging model \|7 (dpeaa)DE-He213
650		4	\|a Gaussian mixture \|7 (dpeaa)DE-He213
700	1		\|a Shayanfar, Mohsen Ali \|4 aut
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700	1		\|a Bakhshpoori, Taha \|4 aut
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allfields	10.1007/s40996-018-0143-y doi (DE-627)SPR038045796 (SPR)s40996-018-0143-y-e DE-627 ger DE-627 rakwb eng Barkhori, Moien verfasserin aut Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2018 Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. Reliability analysis (dpeaa)DE-He213 Adaptive importance sampling (dpeaa)DE-He213 Cross-entropy (dpeaa)DE-He213 Kriging model (dpeaa)DE-He213 Gaussian mixture (dpeaa)DE-He213 Shayanfar, Mohsen Ali aut Barkhordari, Mohammad Ali aut Bakhshpoori, Taha aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:43 year:2018 number:Suppl 1 day:04 month:09 pages:81-88 https://dx.doi.org/10.1007/s40996-018-0143-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2018 Suppl 1 04 09 81-88
spelling	10.1007/s40996-018-0143-y doi (DE-627)SPR038045796 (SPR)s40996-018-0143-y-e DE-627 ger DE-627 rakwb eng Barkhori, Moien verfasserin aut Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2018 Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. Reliability analysis (dpeaa)DE-He213 Adaptive importance sampling (dpeaa)DE-He213 Cross-entropy (dpeaa)DE-He213 Kriging model (dpeaa)DE-He213 Gaussian mixture (dpeaa)DE-He213 Shayanfar, Mohsen Ali aut Barkhordari, Mohammad Ali aut Bakhshpoori, Taha aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:43 year:2018 number:Suppl 1 day:04 month:09 pages:81-88 https://dx.doi.org/10.1007/s40996-018-0143-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2018 Suppl 1 04 09 81-88
allfields_unstemmed	10.1007/s40996-018-0143-y doi (DE-627)SPR038045796 (SPR)s40996-018-0143-y-e DE-627 ger DE-627 rakwb eng Barkhori, Moien verfasserin aut Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2018 Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. Reliability analysis (dpeaa)DE-He213 Adaptive importance sampling (dpeaa)DE-He213 Cross-entropy (dpeaa)DE-He213 Kriging model (dpeaa)DE-He213 Gaussian mixture (dpeaa)DE-He213 Shayanfar, Mohsen Ali aut Barkhordari, Mohammad Ali aut Bakhshpoori, Taha aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:43 year:2018 number:Suppl 1 day:04 month:09 pages:81-88 https://dx.doi.org/10.1007/s40996-018-0143-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2018 Suppl 1 04 09 81-88
allfieldsGer	10.1007/s40996-018-0143-y doi (DE-627)SPR038045796 (SPR)s40996-018-0143-y-e DE-627 ger DE-627 rakwb eng Barkhori, Moien verfasserin aut Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2018 Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. Reliability analysis (dpeaa)DE-He213 Adaptive importance sampling (dpeaa)DE-He213 Cross-entropy (dpeaa)DE-He213 Kriging model (dpeaa)DE-He213 Gaussian mixture (dpeaa)DE-He213 Shayanfar, Mohsen Ali aut Barkhordari, Mohammad Ali aut Bakhshpoori, Taha aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:43 year:2018 number:Suppl 1 day:04 month:09 pages:81-88 https://dx.doi.org/10.1007/s40996-018-0143-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2018 Suppl 1 04 09 81-88
allfieldsSound	10.1007/s40996-018-0143-y doi (DE-627)SPR038045796 (SPR)s40996-018-0143-y-e DE-627 ger DE-627 rakwb eng Barkhori, Moien verfasserin aut Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2018 Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. Reliability analysis (dpeaa)DE-He213 Adaptive importance sampling (dpeaa)DE-He213 Cross-entropy (dpeaa)DE-He213 Kriging model (dpeaa)DE-He213 Gaussian mixture (dpeaa)DE-He213 Shayanfar, Mohsen Ali aut Barkhordari, Mohammad Ali aut Bakhshpoori, Taha aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:43 year:2018 number:Suppl 1 day:04 month:09 pages:81-88 https://dx.doi.org/10.1007/s40996-018-0143-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2018 Suppl 1 04 09 81-88
language	English
source	Enthalten in Iranian journal of science and technology 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88 volume:43 year:2018 number:Suppl 1 day:04 month:09 pages:81-88
sourceStr	Enthalten in Iranian journal of science and technology 43(2018), Suppl 1 vom: 04. Sept., Seite 81-88 volume:43 year:2018 number:Suppl 1 day:04 month:09 pages:81-88
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Reliability analysis Adaptive importance sampling Cross-entropy Kriging model Gaussian mixture
isfreeaccess_bool	false
container_title	Iranian journal of science and technology
authorswithroles_txt_mv	Barkhori, Moien @@aut@@ Shayanfar, Mohsen Ali @@aut@@ Barkhordari, Mohammad Ali @@aut@@ Bakhshpoori, Taha @@aut@@
publishDateDaySort_date	2018-09-04T00:00:00Z
hierarchy_top_id	844238023
id	SPR038045796
language_de	englisch
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author	Barkhori, Moien
spellingShingle	Barkhori, Moien misc Reliability analysis misc Adaptive importance sampling misc Cross-entropy misc Kriging model misc Gaussian mixture Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture
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topic_title	Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture Reliability analysis (dpeaa)DE-He213 Adaptive importance sampling (dpeaa)DE-He213 Cross-entropy (dpeaa)DE-He213 Kriging model (dpeaa)DE-He213 Gaussian mixture (dpeaa)DE-He213
topic	misc Reliability analysis misc Adaptive importance sampling misc Cross-entropy misc Kriging model misc Gaussian mixture
topic_unstemmed	misc Reliability analysis misc Adaptive importance sampling misc Cross-entropy misc Kriging model misc Gaussian mixture
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title	Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture
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title_full	Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture
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author_browse	Barkhori, Moien Shayanfar, Mohsen Ali Barkhordari, Mohammad Ali Bakhshpoori, Taha
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title_sort	kriging-aided cross-entropy-based adaptive importance sampling using gaussian mixture
title_auth	Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture
abstract	Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. © Shiraz University 2018
abstractGer	Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. © Shiraz University 2018
abstract_unstemmed	Abstract In the reliability analysis, rare-event probability estimation imposes serious difficulties on conventional simulation methods like crude Monte Carlo. Several advanced variance reduction techniques such as importance sampling and importance splitting are presented to address this issue. Even though these methods require a low number of samples compared with crude Monte Carlo of the same accuracy, their implementation on time-consuming simulation codes is still very demanding. A joint employment of sampling methods and surrogate models is a proper solution. In this study, we integrate the Kriging surrogate model with a cross-entropy-based adaptive importance sampling, which uses the Gaussian mixture as the auxiliary sampling function. The novelty resides in efficient integration of the importance sampling and Kriging model. Results of various presented tests show a remarkable improvement with respect to importance sampling without surrogate. The statistical analysis results reveal that this advantage is achievable without any significant loss of accuracy. © Shiraz University 2018
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title_short	Kriging-Aided Cross-Entropy-Based Adaptive Importance Sampling Using Gaussian Mixture
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author2	Shayanfar, Mohsen Ali Barkhordari, Mohammad Ali Bakhshpoori, Taha
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up_date	2024-07-03T15:54:09.011Z
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