A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search

Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed da...
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Autor*in:	Lee, Hyeonchan [verfasserIn] Kim, Wongon Son, Hyejeong Choi, Hyunhee Jo, Soo-Ho Youn, Byeng D.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bayesian model calibration Digital twin Initial point search algorithm

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Structural and multidisciplinary optimization - Berlin : Springer, 1989, 66(2023), 6 vom: 12. Mai
Übergeordnetes Werk:	volume:66 ; year:2023 ; number:6 ; day:12 ; month:05

Links:	Volltext

DOI / URN:	10.1007/s00158-023-03577-x

Katalog-ID:	SPR051938251

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520			\|a Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer.
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912			\|a GBV_ILN_281
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912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
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912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
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912			\|a GBV_ILN_2108
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allfields	10.1007/s00158-023-03577-x doi (DE-627)SPR051938251 (SPR)s00158-023-03577-x-e DE-627 ger DE-627 rakwb eng Lee, Hyeonchan verfasserin aut A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. Bayesian model calibration (dpeaa)DE-He213 Digital twin (dpeaa)DE-He213 Initial point search algorithm (dpeaa)DE-He213 Kim, Wongon (orcid)0000-0003-0135-3660 aut Son, Hyejeong aut Choi, Hyunhee aut Jo, Soo-Ho aut Youn, Byeng D. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 66(2023), 6 vom: 12. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:66 year:2023 number:6 day:12 month:05 https://dx.doi.org/10.1007/s00158-023-03577-x 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_2122 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 6 12 05
spelling	10.1007/s00158-023-03577-x doi (DE-627)SPR051938251 (SPR)s00158-023-03577-x-e DE-627 ger DE-627 rakwb eng Lee, Hyeonchan verfasserin aut A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. Bayesian model calibration (dpeaa)DE-He213 Digital twin (dpeaa)DE-He213 Initial point search algorithm (dpeaa)DE-He213 Kim, Wongon (orcid)0000-0003-0135-3660 aut Son, Hyejeong aut Choi, Hyunhee aut Jo, Soo-Ho aut Youn, Byeng D. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 66(2023), 6 vom: 12. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:66 year:2023 number:6 day:12 month:05 https://dx.doi.org/10.1007/s00158-023-03577-x 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_2122 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 6 12 05
allfields_unstemmed	10.1007/s00158-023-03577-x doi (DE-627)SPR051938251 (SPR)s00158-023-03577-x-e DE-627 ger DE-627 rakwb eng Lee, Hyeonchan verfasserin aut A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. Bayesian model calibration (dpeaa)DE-He213 Digital twin (dpeaa)DE-He213 Initial point search algorithm (dpeaa)DE-He213 Kim, Wongon (orcid)0000-0003-0135-3660 aut Son, Hyejeong aut Choi, Hyunhee aut Jo, Soo-Ho aut Youn, Byeng D. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 66(2023), 6 vom: 12. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:66 year:2023 number:6 day:12 month:05 https://dx.doi.org/10.1007/s00158-023-03577-x 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_2122 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 6 12 05
allfieldsGer	10.1007/s00158-023-03577-x doi (DE-627)SPR051938251 (SPR)s00158-023-03577-x-e DE-627 ger DE-627 rakwb eng Lee, Hyeonchan verfasserin aut A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. Bayesian model calibration (dpeaa)DE-He213 Digital twin (dpeaa)DE-He213 Initial point search algorithm (dpeaa)DE-He213 Kim, Wongon (orcid)0000-0003-0135-3660 aut Son, Hyejeong aut Choi, Hyunhee aut Jo, Soo-Ho aut Youn, Byeng D. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 66(2023), 6 vom: 12. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:66 year:2023 number:6 day:12 month:05 https://dx.doi.org/10.1007/s00158-023-03577-x 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_2122 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 6 12 05
allfieldsSound	10.1007/s00158-023-03577-x doi (DE-627)SPR051938251 (SPR)s00158-023-03577-x-e DE-627 ger DE-627 rakwb eng Lee, Hyeonchan verfasserin aut A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. Bayesian model calibration (dpeaa)DE-He213 Digital twin (dpeaa)DE-He213 Initial point search algorithm (dpeaa)DE-He213 Kim, Wongon (orcid)0000-0003-0135-3660 aut Son, Hyejeong aut Choi, Hyunhee aut Jo, Soo-Ho aut Youn, Byeng D. aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 66(2023), 6 vom: 12. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:66 year:2023 number:6 day:12 month:05 https://dx.doi.org/10.1007/s00158-023-03577-x 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_2056 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_2122 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 6 12 05
language	English
source	Enthalten in Structural and multidisciplinary optimization 66(2023), 6 vom: 12. Mai volume:66 year:2023 number:6 day:12 month:05
sourceStr	Enthalten in Structural and multidisciplinary optimization 66(2023), 6 vom: 12. Mai volume:66 year:2023 number:6 day:12 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bayesian model calibration Digital twin Initial point search algorithm
isfreeaccess_bool	false
container_title	Structural and multidisciplinary optimization
authorswithroles_txt_mv	Lee, Hyeonchan @@aut@@ Kim, Wongon @@aut@@ Son, Hyejeong @@aut@@ Choi, Hyunhee @@aut@@ Jo, Soo-Ho @@aut@@ Youn, Byeng D. @@aut@@
publishDateDaySort_date	2023-05-12T00:00:00Z
hierarchy_top_id	271602503
id	SPR051938251
language_de	englisch
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author	Lee, Hyeonchan
spellingShingle	Lee, Hyeonchan misc Bayesian model calibration misc Digital twin misc Initial point search algorithm A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search
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topic_title	A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search Bayesian model calibration (dpeaa)DE-He213 Digital twin (dpeaa)DE-He213 Initial point search algorithm (dpeaa)DE-He213
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topic_unstemmed	misc Bayesian model calibration misc Digital twin misc Initial point search algorithm
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title	A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search
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title_full	A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search
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author_browse	Lee, Hyeonchan Kim, Wongon Son, Hyejeong Choi, Hyunhee Jo, Soo-Ho Youn, Byeng D.
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author-letter	Lee, Hyeonchan
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title_sort	new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search
title_auth	A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search
abstract	Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search
url	https://dx.doi.org/10.1007/s00158-023-03577-x
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author2	Kim, Wongon Son, Hyejeong Choi, Hyunhee Jo, Soo-Ho Youn, Byeng D.
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up_date	2024-07-04T00:31:19.388Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR051938251</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230726103742.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230618s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00158-023-03577-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051938251</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00158-023-03577-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lee, Hyeonchan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Digital Twin (DTw) model is a numerical model in a virtual world that supports engineer decisions using observed data from a real system. However, uncertainty in the physical model parameters of DTw degrades the predictive performance of a DTw. Bayesian calibration utilizes both observed data and prior knowledge to estimate uncertain model parameters in a statistical manner using Bayes’ theorem. Markov Chain Monte Carlo (MCMC) is an effective searching algorithm that can be used to estimate a complex posterior distribution. In the MCMC method, the point that is used to initiate the MCMC sampling significantly affects the burn-in period impacting the accuracy and efficiency of the estimation. However, a proper initial point is hard to select because of the computational cost of searching high-dimensional parameter space. Previous optimization algorithms or random sampling algorithms have focused on solution convergence for a local or global optimum solution. However, the initial points searching method for DTw required suggesting multiple feasible optimum points where a solution can be existed to make proper engineering decisions based on DTw analysis based on each optimum. This paper describes the development of a cost-effective, stochastic algorithm, called the Greedy Stochastic Section Search (GSSS) algorithm that can systematically explore high-dimensional parametric space to select proper initial points for DTw. We verified the new algorithm's performance by applying it to a numerical example with a Mixture of Gaussian (MoG) 6 and by calibrating an engineering example, specifically a digital twin approach for an on-load tap changer.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian model calibration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Digital twin</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Initial point search algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kim, Wongon</subfield><subfield code="0">(orcid)0000-0003-0135-3660</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Son, Hyejeong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Choi, Hyunhee</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jo, Soo-Ho</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Youn, Byeng D.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Structural and multidisciplinary optimization</subfield><subfield code="d">Berlin : Springer, 1989</subfield><subfield code="g">66(2023), 6 vom: 12. 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A new initial point search algorithm for bayesian calibration with insufficient statistical information: greedy stochastic section search

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