Data assimilation with hybrid modeling

Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent e...
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Gespeichert in:

Autor*in:	Shao, Dongrui [verfasserIn] Chu, Junyu [verfasserIn] Chen, Luonan [verfasserIn] Ma, Huanfei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Kalman filter Reservoir computing Delay embedding Membrane potential Data assimilation

Übergeordnetes Werk:	Enthalten in: Chaos, solitons & fractals - Amsterdam [u.a.] : Elsevier Science, 1991, 167
Übergeordnetes Werk:	volume:167

DOI / URN:	10.1016/j.chaos.2022.113069

Katalog-ID:	ELV00913848X

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520			\|a Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method.
650		4	\|a Kalman filter
650		4	\|a Reservoir computing
650		4	\|a Delay embedding
650		4	\|a Membrane potential
650		4	\|a Data assimilation
700	1		\|a Chu, Junyu \|e verfasserin \|4 aut
700	1		\|a Chen, Luonan \|e verfasserin \|4 aut
700	1		\|a Ma, Huanfei \|e verfasserin \|0 (orcid)0000-0002-4262-0123 \|4 aut
773	0	8	\|i Enthalten in \|t Chaos, solitons & fractals \|d Amsterdam [u.a.] : Elsevier Science, 1991 \|g 167 \|h Online-Ressource \|w (DE-627)314118497 \|w (DE-600)2003919-0 \|w (DE-576)094504040 \|x 1873-2887 \|7 nnns
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936	b	k	\|a 30.20 \|j Nichtlineare Dynamik
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bklnumber	30.20 31.00
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allfields	10.1016/j.chaos.2022.113069 doi (DE-627)ELV00913848X (ELSEVIER)S0960-0779(22)01248-6 DE-627 ger DE-627 rda eng 510 DE-600 30.20 bkl 31.00 bkl Shao, Dongrui verfasserin aut Data assimilation with hybrid modeling 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method. Kalman filter Reservoir computing Delay embedding Membrane potential Data assimilation Chu, Junyu verfasserin aut Chen, Luonan verfasserin aut Ma, Huanfei verfasserin (orcid)0000-0002-4262-0123 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 167 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:167 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 30.20 Nichtlineare Dynamik 31.00 Mathematik: Allgemeines AR 167
spelling	10.1016/j.chaos.2022.113069 doi (DE-627)ELV00913848X (ELSEVIER)S0960-0779(22)01248-6 DE-627 ger DE-627 rda eng 510 DE-600 30.20 bkl 31.00 bkl Shao, Dongrui verfasserin aut Data assimilation with hybrid modeling 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method. Kalman filter Reservoir computing Delay embedding Membrane potential Data assimilation Chu, Junyu verfasserin aut Chen, Luonan verfasserin aut Ma, Huanfei verfasserin (orcid)0000-0002-4262-0123 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 167 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:167 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 30.20 Nichtlineare Dynamik 31.00 Mathematik: Allgemeines AR 167
allfields_unstemmed	10.1016/j.chaos.2022.113069 doi (DE-627)ELV00913848X (ELSEVIER)S0960-0779(22)01248-6 DE-627 ger DE-627 rda eng 510 DE-600 30.20 bkl 31.00 bkl Shao, Dongrui verfasserin aut Data assimilation with hybrid modeling 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method. Kalman filter Reservoir computing Delay embedding Membrane potential Data assimilation Chu, Junyu verfasserin aut Chen, Luonan verfasserin aut Ma, Huanfei verfasserin (orcid)0000-0002-4262-0123 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 167 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:167 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 30.20 Nichtlineare Dynamik 31.00 Mathematik: Allgemeines AR 167
allfieldsGer	10.1016/j.chaos.2022.113069 doi (DE-627)ELV00913848X (ELSEVIER)S0960-0779(22)01248-6 DE-627 ger DE-627 rda eng 510 DE-600 30.20 bkl 31.00 bkl Shao, Dongrui verfasserin aut Data assimilation with hybrid modeling 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method. Kalman filter Reservoir computing Delay embedding Membrane potential Data assimilation Chu, Junyu verfasserin aut Chen, Luonan verfasserin aut Ma, Huanfei verfasserin (orcid)0000-0002-4262-0123 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 167 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:167 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 30.20 Nichtlineare Dynamik 31.00 Mathematik: Allgemeines AR 167
allfieldsSound	10.1016/j.chaos.2022.113069 doi (DE-627)ELV00913848X (ELSEVIER)S0960-0779(22)01248-6 DE-627 ger DE-627 rda eng 510 DE-600 30.20 bkl 31.00 bkl Shao, Dongrui verfasserin aut Data assimilation with hybrid modeling 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method. Kalman filter Reservoir computing Delay embedding Membrane potential Data assimilation Chu, Junyu verfasserin aut Chen, Luonan verfasserin aut Ma, Huanfei verfasserin (orcid)0000-0002-4262-0123 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 167 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:167 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 30.20 Nichtlineare Dynamik 31.00 Mathematik: Allgemeines AR 167
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authorswithroles_txt_mv	Shao, Dongrui @@aut@@ Chu, Junyu @@aut@@ Chen, Luonan @@aut@@ Ma, Huanfei @@aut@@
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author	Shao, Dongrui
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topic_title	510 DE-600 30.20 bkl 31.00 bkl Data assimilation with hybrid modeling Kalman filter Reservoir computing Delay embedding Membrane potential Data assimilation
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title_sort	data assimilation with hybrid modeling
title_auth	Data assimilation with hybrid modeling
abstract	Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method.
abstractGer	Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method.
abstract_unstemmed	Data assimilation plays an important role in both data driven and model driven research. The celebrated Kalman filter, a typical data assimilation framework, has been widely adopted in many fields. While the classic Kalman filter relies on the theoretical model to realize filtering, several recent efforts have been made to design model-free Kalman filter which rely solely on data. In this work, we consider the gap between exact model-based method and totally model-free method, and carry out a hybrid model framework to deal with partial model and partial observation scenario. Specifically, we propose a method combining both delay embedding theory and machine learning technique to reconstruct the missing model part and such hybrid modeling is then integrated into the adaptive unscented Kalman filter framework. Overall, the hybrid modeling method is more flexible in application compared to both model-based and model-free methods. With both benchmark systems and real-world problems, we validate the effectiveness of the proposed method.
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title_short	Data assimilation with hybrid modeling
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author2	Chu, Junyu Chen, Luonan Ma, Huanfei
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doi_str	10.1016/j.chaos.2022.113069
up_date	2024-07-06T22:07:42.271Z
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