A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR)
Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Oh, Hyunseok [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Structural and multidisciplinary optimization - Berlin : Springer, 1989, 60(2019), 3 vom: 06. Mai, Seite 1171-1187 |
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| Übergeordnetes Werk: |
volume:60 ; year:2019 ; number:3 ; day:06 ; month:05 ; pages:1171-1187 |
| Links: |
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| DOI / URN: |
10.1007/s00158-019-02288-6 |
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| Katalog-ID: |
SPR001331019 |
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| 100 | 1 | |a Oh, Hyunseok |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) |
| 264 | 1 | |c 2019 | |
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| 520 | |a Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. | ||
| 650 | 4 | |a Computational model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Statistical model calibration |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Calibration metric |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Validity check |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Journal bearing rotor system |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Choi, Hwanoh |4 aut | |
| 700 | 1 | |a Jung, Joon Ha |4 aut | |
| 700 | 1 | |a Youn, Byeng D. |0 (orcid)0000-0003-0135-3660 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Structural and multidisciplinary optimization |d Berlin : Springer, 1989 |g 60(2019), 3 vom: 06. Mai, Seite 1171-1187 |w (DE-627)271602503 |w (DE-600)1481279-4 |x 1615-1488 |7 nnns |
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10.1007/s00158-019-02288-6 doi (DE-627)SPR001331019 (SPR)s00158-019-02288-6-e DE-627 ger DE-627 rakwb eng Oh, Hyunseok verfasserin aut A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. Computational model (dpeaa)DE-He213 Statistical model calibration (dpeaa)DE-He213 Calibration metric (dpeaa)DE-He213 Validity check (dpeaa)DE-He213 Journal bearing rotor system (dpeaa)DE-He213 Choi, Hwanoh aut Jung, Joon Ha aut Youn, Byeng D. (orcid)0000-0003-0135-3660 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 60(2019), 3 vom: 06. Mai, Seite 1171-1187 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:60 year:2019 number:3 day:06 month:05 pages:1171-1187 https://dx.doi.org/10.1007/s00158-019-02288-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 60 2019 3 06 05 1171-1187 |
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10.1007/s00158-019-02288-6 doi (DE-627)SPR001331019 (SPR)s00158-019-02288-6-e DE-627 ger DE-627 rakwb eng Oh, Hyunseok verfasserin aut A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. Computational model (dpeaa)DE-He213 Statistical model calibration (dpeaa)DE-He213 Calibration metric (dpeaa)DE-He213 Validity check (dpeaa)DE-He213 Journal bearing rotor system (dpeaa)DE-He213 Choi, Hwanoh aut Jung, Joon Ha aut Youn, Byeng D. (orcid)0000-0003-0135-3660 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 60(2019), 3 vom: 06. Mai, Seite 1171-1187 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:60 year:2019 number:3 day:06 month:05 pages:1171-1187 https://dx.doi.org/10.1007/s00158-019-02288-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 60 2019 3 06 05 1171-1187 |
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10.1007/s00158-019-02288-6 doi (DE-627)SPR001331019 (SPR)s00158-019-02288-6-e DE-627 ger DE-627 rakwb eng Oh, Hyunseok verfasserin aut A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. Computational model (dpeaa)DE-He213 Statistical model calibration (dpeaa)DE-He213 Calibration metric (dpeaa)DE-He213 Validity check (dpeaa)DE-He213 Journal bearing rotor system (dpeaa)DE-He213 Choi, Hwanoh aut Jung, Joon Ha aut Youn, Byeng D. (orcid)0000-0003-0135-3660 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 60(2019), 3 vom: 06. Mai, Seite 1171-1187 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:60 year:2019 number:3 day:06 month:05 pages:1171-1187 https://dx.doi.org/10.1007/s00158-019-02288-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 60 2019 3 06 05 1171-1187 |
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10.1007/s00158-019-02288-6 doi (DE-627)SPR001331019 (SPR)s00158-019-02288-6-e DE-627 ger DE-627 rakwb eng Oh, Hyunseok verfasserin aut A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. Computational model (dpeaa)DE-He213 Statistical model calibration (dpeaa)DE-He213 Calibration metric (dpeaa)DE-He213 Validity check (dpeaa)DE-He213 Journal bearing rotor system (dpeaa)DE-He213 Choi, Hwanoh aut Jung, Joon Ha aut Youn, Byeng D. (orcid)0000-0003-0135-3660 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 60(2019), 3 vom: 06. Mai, Seite 1171-1187 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:60 year:2019 number:3 day:06 month:05 pages:1171-1187 https://dx.doi.org/10.1007/s00158-019-02288-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 60 2019 3 06 05 1171-1187 |
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10.1007/s00158-019-02288-6 doi (DE-627)SPR001331019 (SPR)s00158-019-02288-6-e DE-627 ger DE-627 rakwb eng Oh, Hyunseok verfasserin aut A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. Computational model (dpeaa)DE-He213 Statistical model calibration (dpeaa)DE-He213 Calibration metric (dpeaa)DE-He213 Validity check (dpeaa)DE-He213 Journal bearing rotor system (dpeaa)DE-He213 Choi, Hwanoh aut Jung, Joon Ha aut Youn, Byeng D. (orcid)0000-0003-0135-3660 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 60(2019), 3 vom: 06. Mai, Seite 1171-1187 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:60 year:2019 number:3 day:06 month:05 pages:1171-1187 https://dx.doi.org/10.1007/s00158-019-02288-6 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 60 2019 3 06 05 1171-1187 |
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Oh, Hyunseok |
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A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) Computational model (dpeaa)DE-He213 Statistical model calibration (dpeaa)DE-He213 Calibration metric (dpeaa)DE-He213 Validity check (dpeaa)DE-He213 Journal bearing rotor system (dpeaa)DE-He213 |
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robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (pr) |
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A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) |
| abstract |
Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstractGer |
Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Statistical model calibration is a practical tool for computational model development processes. However, in optimization-based model calibration, the quality of the calibrated model is often unsatisfactory due to inefficiency and/or inaccuracy of calibration metrics. This paper proposes a new calibration metric, namely, probability residual (PR). PR quantifies the degree of agreement or disagreement between the computational response and experimental results. The PR metric is defined as the sum of the product of a scale factor and the squared residual. First, the scale factor defines the shape of the squared residual to maintain consistent sensitivity during the optimization process. Thus, the number of function evaluations can be reduced. Second, the mathematical form of the squared residuals is used to make convex optimization feasible. Therefore, the existence of a global minimum is guaranteed. To evaluate the performance of the proposed metric, numerical examples are shown in a case study. Various system functions—including linear, non-linear, and elliptical—are incorporated into the statistical model calibration. A case study that examines journal bearing rotor systems is presented to demonstrate the application of the proposed calibration metric to a real-world engineered system. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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A robust and convex metric for unconstrained optimization in statistical model calibration—probability residual (PR) |
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https://dx.doi.org/10.1007/s00158-019-02288-6 |
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Choi, Hwanoh Jung, Joon Ha Youn, Byeng D. |
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Choi, Hwanoh Jung, Joon Ha Youn, Byeng D. |
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| doi_str |
10.1007/s00158-019-02288-6 |
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2024-07-03T21:50:42.925Z |
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| score |
7.3996744 |