Data-driven discovery of governing equations for transient heat transfer analysis
Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based seque...
Ausführliche Beschreibung
Autor*in: |
Jin, Guodong [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
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Übergeordnetes Werk: |
Enthalten in: Computational geosciences - New York, NY [u.a.] : Springer Science + Business Media B.V., 1997, 26(2022), 3 vom: 15. Apr., Seite 613-631 |
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Übergeordnetes Werk: |
volume:26 ; year:2022 ; number:3 ; day:15 ; month:04 ; pages:613-631 |
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DOI / URN: |
10.1007/s10596-022-10145-7 |
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Katalog-ID: |
SPR046942076 |
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10.1007/s10596-022-10145-7 doi (DE-627)SPR046942076 (SPR)s10596-022-10145-7-e DE-627 ger DE-627 rakwb eng Jin, Guodong verfasserin aut Data-driven discovery of governing equations for transient heat transfer analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. Data-driven (dpeaa)DE-He213 Governing equations (dpeaa)DE-He213 Sparse regression (dpeaa)DE-He213 Heat transfer (dpeaa)DE-He213 Numerical analysis (dpeaa)DE-He213 Xing, Huilin aut Zhang, Rongxin aut Guo, Zhiwei aut Liu, Junbiao aut Enthalten in Computational geosciences New York, NY [u.a.] : Springer Science + Business Media B.V., 1997 26(2022), 3 vom: 15. Apr., Seite 613-631 (DE-627)312901313 (DE-600)2001545-8 1573-1499 nnns volume:26 year:2022 number:3 day:15 month:04 pages:613-631 https://dx.doi.org/10.1007/s10596-022-10145-7 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_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_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_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_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 26 2022 3 15 04 613-631 |
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10.1007/s10596-022-10145-7 doi (DE-627)SPR046942076 (SPR)s10596-022-10145-7-e DE-627 ger DE-627 rakwb eng Jin, Guodong verfasserin aut Data-driven discovery of governing equations for transient heat transfer analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. Data-driven (dpeaa)DE-He213 Governing equations (dpeaa)DE-He213 Sparse regression (dpeaa)DE-He213 Heat transfer (dpeaa)DE-He213 Numerical analysis (dpeaa)DE-He213 Xing, Huilin aut Zhang, Rongxin aut Guo, Zhiwei aut Liu, Junbiao aut Enthalten in Computational geosciences New York, NY [u.a.] : Springer Science + Business Media B.V., 1997 26(2022), 3 vom: 15. Apr., Seite 613-631 (DE-627)312901313 (DE-600)2001545-8 1573-1499 nnns volume:26 year:2022 number:3 day:15 month:04 pages:613-631 https://dx.doi.org/10.1007/s10596-022-10145-7 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_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_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_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_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 26 2022 3 15 04 613-631 |
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10.1007/s10596-022-10145-7 doi (DE-627)SPR046942076 (SPR)s10596-022-10145-7-e DE-627 ger DE-627 rakwb eng Jin, Guodong verfasserin aut Data-driven discovery of governing equations for transient heat transfer analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. Data-driven (dpeaa)DE-He213 Governing equations (dpeaa)DE-He213 Sparse regression (dpeaa)DE-He213 Heat transfer (dpeaa)DE-He213 Numerical analysis (dpeaa)DE-He213 Xing, Huilin aut Zhang, Rongxin aut Guo, Zhiwei aut Liu, Junbiao aut Enthalten in Computational geosciences New York, NY [u.a.] : Springer Science + Business Media B.V., 1997 26(2022), 3 vom: 15. Apr., Seite 613-631 (DE-627)312901313 (DE-600)2001545-8 1573-1499 nnns volume:26 year:2022 number:3 day:15 month:04 pages:613-631 https://dx.doi.org/10.1007/s10596-022-10145-7 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_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_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_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_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 26 2022 3 15 04 613-631 |
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10.1007/s10596-022-10145-7 doi (DE-627)SPR046942076 (SPR)s10596-022-10145-7-e DE-627 ger DE-627 rakwb eng Jin, Guodong verfasserin aut Data-driven discovery of governing equations for transient heat transfer analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. Data-driven (dpeaa)DE-He213 Governing equations (dpeaa)DE-He213 Sparse regression (dpeaa)DE-He213 Heat transfer (dpeaa)DE-He213 Numerical analysis (dpeaa)DE-He213 Xing, Huilin aut Zhang, Rongxin aut Guo, Zhiwei aut Liu, Junbiao aut Enthalten in Computational geosciences New York, NY [u.a.] : Springer Science + Business Media B.V., 1997 26(2022), 3 vom: 15. Apr., Seite 613-631 (DE-627)312901313 (DE-600)2001545-8 1573-1499 nnns volume:26 year:2022 number:3 day:15 month:04 pages:613-631 https://dx.doi.org/10.1007/s10596-022-10145-7 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_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_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_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_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 26 2022 3 15 04 613-631 |
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10.1007/s10596-022-10145-7 doi (DE-627)SPR046942076 (SPR)s10596-022-10145-7-e DE-627 ger DE-627 rakwb eng Jin, Guodong verfasserin aut Data-driven discovery of governing equations for transient heat transfer analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. Data-driven (dpeaa)DE-He213 Governing equations (dpeaa)DE-He213 Sparse regression (dpeaa)DE-He213 Heat transfer (dpeaa)DE-He213 Numerical analysis (dpeaa)DE-He213 Xing, Huilin aut Zhang, Rongxin aut Guo, Zhiwei aut Liu, Junbiao aut Enthalten in Computational geosciences New York, NY [u.a.] : Springer Science + Business Media B.V., 1997 26(2022), 3 vom: 15. Apr., Seite 613-631 (DE-627)312901313 (DE-600)2001545-8 1573-1499 nnns volume:26 year:2022 number:3 day:15 month:04 pages:613-631 https://dx.doi.org/10.1007/s10596-022-10145-7 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_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_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_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_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 26 2022 3 15 04 613-631 |
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data-driven discovery of governing equations for transient heat transfer analysis |
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Data-driven discovery of governing equations for transient heat transfer analysis |
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Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
abstractGer |
Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
abstract_unstemmed |
Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
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Data-driven discovery of governing equations for transient heat transfer analysis |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046942076</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509101726.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220508s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10596-022-10145-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046942076</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10596-022-10145-7-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">Jin, Guodong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data-driven discovery of governing equations for transient heat transfer analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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 Nature Switzerland AG 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract With the development of automatic measurement and data storage, vast quantities of data can be recorded and analyzed for heat transfer processes, which provides an opportunity to discover the transient heat transfer governing laws from the data. In this study, a machine learning-based sequential threshold ridge regression (STRidge) approach is applied to extract partial differential equations (PDEs) and tested on the heat conduction equation and conductive–convective heat transfer equation subjected to different boundary conditions, data volumes, and noise levels. Moreover, we studied the learning of governing equation of nonlinear transient heat transfer and used the improved STRidge with genetic algorithm to learn PDE with incomplete candidate library. The results showcase highly accurate identification of governing equations for heat transfer. And our results reveal the vast potential of the data-driven method in complex geothermal problems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-driven</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Governing equations</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sparse regression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heat transfer</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Numerical analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xing, Huilin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Rongxin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Zhiwei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Junbiao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational geosciences</subfield><subfield code="d">New York, NY [u.a.] : Springer Science + Business Media B.V., 1997</subfield><subfield code="g">26(2022), 3 vom: 15. Apr., Seite 613-631</subfield><subfield code="w">(DE-627)312901313</subfield><subfield code="w">(DE-600)2001545-8</subfield><subfield code="x">1573-1499</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:26</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:3</subfield><subfield code="g">day:15</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:613-631</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10596-022-10145-7</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" 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