Error estimation in coupled multi-physics models
Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Subramanian, Abhinav [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
19 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty - Miranda, Regina ELSEVIER, 2023, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:395 ; year:2019 ; day:15 ; month:10 ; pages:19-37 ; extent:19 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jcp.2019.06.013 |
|---|
| Katalog-ID: |
ELV047531398 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV047531398 | ||
| 003 | DE-627 | ||
| 005 | 20230626015903.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191021s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jcp.2019.06.013 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000711.pica |
| 035 | |a (DE-627)ELV047531398 | ||
| 035 | |a (ELSEVIER)S0021-9991(19)30420-6 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.91 |2 bkl | ||
| 100 | 1 | |a Subramanian, Abhinav |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Error estimation in coupled multi-physics models |
| 264 | 1 | |c 2019transfer abstract | |
| 300 | |a 19 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. | ||
| 520 | |a Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. | ||
| 650 | 7 | |a Multi-disciplinary analysis |2 Elsevier | |
| 650 | 7 | |a Hypersonic aircraft panel |2 Elsevier | |
| 650 | 7 | |a Model form error |2 Elsevier | |
| 650 | 7 | |a Discrepancy estimation |2 Elsevier | |
| 650 | 7 | |a Bayesian state estimation |2 Elsevier | |
| 700 | 1 | |a Mahadevan, Sankaran |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Miranda, Regina ELSEVIER |t Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty |d 2023 |g Amsterdam |w (DE-627)ELV010178430 |
| 773 | 1 | 8 | |g volume:395 |g year:2019 |g day:15 |g month:10 |g pages:19-37 |g extent:19 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jcp.2019.06.013 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_90 | ||
| 936 | b | k | |a 44.91 |j Psychiatrie |j Psychopathologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 395 |j 2019 |b 15 |c 1015 |h 19-37 |g 19 | ||
| author_variant |
a s as |
|---|---|
| matchkey_str |
subramanianabhinavmahadevansankaran:2019----:roetmtoiculdutp |
| hierarchy_sort_str |
2019transfer abstract |
| bklnumber |
44.91 |
| publishDate |
2019 |
| allfields |
10.1016/j.jcp.2019.06.013 doi GBV00000000000711.pica (DE-627)ELV047531398 (ELSEVIER)S0021-9991(19)30420-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Subramanian, Abhinav verfasserin aut Error estimation in coupled multi-physics models 2019transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation Elsevier Mahadevan, Sankaran oth Enthalten in Elsevier Miranda, Regina ELSEVIER Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty 2023 Amsterdam (DE-627)ELV010178430 volume:395 year:2019 day:15 month:10 pages:19-37 extent:19 https://doi.org/10.1016/j.jcp.2019.06.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_24 GBV_ILN_90 44.91 Psychiatrie Psychopathologie VZ AR 395 2019 15 1015 19-37 19 |
| spelling |
10.1016/j.jcp.2019.06.013 doi GBV00000000000711.pica (DE-627)ELV047531398 (ELSEVIER)S0021-9991(19)30420-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Subramanian, Abhinav verfasserin aut Error estimation in coupled multi-physics models 2019transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation Elsevier Mahadevan, Sankaran oth Enthalten in Elsevier Miranda, Regina ELSEVIER Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty 2023 Amsterdam (DE-627)ELV010178430 volume:395 year:2019 day:15 month:10 pages:19-37 extent:19 https://doi.org/10.1016/j.jcp.2019.06.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_24 GBV_ILN_90 44.91 Psychiatrie Psychopathologie VZ AR 395 2019 15 1015 19-37 19 |
| allfields_unstemmed |
10.1016/j.jcp.2019.06.013 doi GBV00000000000711.pica (DE-627)ELV047531398 (ELSEVIER)S0021-9991(19)30420-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Subramanian, Abhinav verfasserin aut Error estimation in coupled multi-physics models 2019transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation Elsevier Mahadevan, Sankaran oth Enthalten in Elsevier Miranda, Regina ELSEVIER Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty 2023 Amsterdam (DE-627)ELV010178430 volume:395 year:2019 day:15 month:10 pages:19-37 extent:19 https://doi.org/10.1016/j.jcp.2019.06.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_24 GBV_ILN_90 44.91 Psychiatrie Psychopathologie VZ AR 395 2019 15 1015 19-37 19 |
| allfieldsGer |
10.1016/j.jcp.2019.06.013 doi GBV00000000000711.pica (DE-627)ELV047531398 (ELSEVIER)S0021-9991(19)30420-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Subramanian, Abhinav verfasserin aut Error estimation in coupled multi-physics models 2019transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation Elsevier Mahadevan, Sankaran oth Enthalten in Elsevier Miranda, Regina ELSEVIER Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty 2023 Amsterdam (DE-627)ELV010178430 volume:395 year:2019 day:15 month:10 pages:19-37 extent:19 https://doi.org/10.1016/j.jcp.2019.06.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_24 GBV_ILN_90 44.91 Psychiatrie Psychopathologie VZ AR 395 2019 15 1015 19-37 19 |
| allfieldsSound |
10.1016/j.jcp.2019.06.013 doi GBV00000000000711.pica (DE-627)ELV047531398 (ELSEVIER)S0021-9991(19)30420-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Subramanian, Abhinav verfasserin aut Error estimation in coupled multi-physics models 2019transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation Elsevier Mahadevan, Sankaran oth Enthalten in Elsevier Miranda, Regina ELSEVIER Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty 2023 Amsterdam (DE-627)ELV010178430 volume:395 year:2019 day:15 month:10 pages:19-37 extent:19 https://doi.org/10.1016/j.jcp.2019.06.013 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_24 GBV_ILN_90 44.91 Psychiatrie Psychopathologie VZ AR 395 2019 15 1015 19-37 19 |
| language |
English |
| source |
Enthalten in Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty Amsterdam volume:395 year:2019 day:15 month:10 pages:19-37 extent:19 |
| sourceStr |
Enthalten in Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty Amsterdam volume:395 year:2019 day:15 month:10 pages:19-37 extent:19 |
| format_phy_str_mv |
Article |
| bklname |
Psychiatrie Psychopathologie |
| institution |
findex.gbv.de |
| topic_facet |
Multi-disciplinary analysis Hypersonic aircraft panel Model form error Discrepancy estimation Bayesian state estimation |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty |
| authorswithroles_txt_mv |
Subramanian, Abhinav @@aut@@ Mahadevan, Sankaran @@oth@@ |
| publishDateDaySort_date |
2019-01-15T00:00:00Z |
| hierarchy_top_id |
ELV010178430 |
| dewey-sort |
3610 |
| id |
ELV047531398 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV047531398</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626015903.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191021s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jcp.2019.06.013</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000711.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047531398</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0021-9991(19)30420-6</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="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.91</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Subramanian, Abhinav</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Error estimation in coupled multi-physics models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">19</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multi-disciplinary analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hypersonic aircraft panel</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Model form error</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Discrepancy estimation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Bayesian state estimation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mahadevan, Sankaran</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Miranda, Regina ELSEVIER</subfield><subfield code="t">Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty</subfield><subfield code="d">2023</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV010178430</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:395</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:15</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:19-37</subfield><subfield code="g">extent:19</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jcp.2019.06.013</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.91</subfield><subfield code="j">Psychiatrie</subfield><subfield code="j">Psychopathologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">395</subfield><subfield code="j">2019</subfield><subfield code="b">15</subfield><subfield code="c">1015</subfield><subfield code="h">19-37</subfield><subfield code="g">19</subfield></datafield></record></collection>
|
| author |
Subramanian, Abhinav |
| spellingShingle |
Subramanian, Abhinav ddc 610 bkl 44.91 Elsevier Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation Error estimation in coupled multi-physics models |
| authorStr |
Subramanian, Abhinav |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV010178430 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 44.91 bkl Error estimation in coupled multi-physics models Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation Elsevier |
| topic |
ddc 610 bkl 44.91 Elsevier Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation |
| topic_unstemmed |
ddc 610 bkl 44.91 Elsevier Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation |
| topic_browse |
ddc 610 bkl 44.91 Elsevier Multi-disciplinary analysis Elsevier Hypersonic aircraft panel Elsevier Model form error Elsevier Discrepancy estimation Elsevier Bayesian state estimation |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
s m sm |
| hierarchy_parent_title |
Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty |
| hierarchy_parent_id |
ELV010178430 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV010178430 |
| title |
Error estimation in coupled multi-physics models |
| ctrlnum |
(DE-627)ELV047531398 (ELSEVIER)S0021-9991(19)30420-6 |
| title_full |
Error estimation in coupled multi-physics models |
| author_sort |
Subramanian, Abhinav |
| journal |
Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty |
| journalStr |
Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
zzz |
| container_start_page |
19 |
| author_browse |
Subramanian, Abhinav |
| container_volume |
395 |
| physical |
19 |
| class |
610 VZ 44.91 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Subramanian, Abhinav |
| doi_str_mv |
10.1016/j.jcp.2019.06.013 |
| dewey-full |
610 |
| title_sort |
error estimation in coupled multi-physics models |
| title_auth |
Error estimation in coupled multi-physics models |
| abstract |
Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. |
| abstractGer |
Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. |
| abstract_unstemmed |
Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_24 GBV_ILN_90 |
| title_short |
Error estimation in coupled multi-physics models |
| url |
https://doi.org/10.1016/j.jcp.2019.06.013 |
| remote_bool |
true |
| author2 |
Mahadevan, Sankaran |
| author2Str |
Mahadevan, Sankaran |
| ppnlink |
ELV010178430 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.jcp.2019.06.013 |
| up_date |
2024-07-06T23:08:31.653Z |
| _version_ |
1803872959625953280 |
| 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">ELV047531398</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626015903.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191021s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jcp.2019.06.013</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000711.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047531398</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0021-9991(19)30420-6</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="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.91</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Subramanian, Abhinav</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Error estimation in coupled multi-physics models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">19</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Model predictions often exhibit discrepancy with respect to experimental observations, due to assumptions and approximations in the model. Bayesian approaches for estimating discrepancy in single models have been studied in the past. In this paper, we approach the problem of discrepancy estimation in coupled models (especially multi-disciplinary models) using Bayesian state estimation methods. The proposed state estimation-based approach is found to have significant advantages over the previously studied Kennedy-O'Hagan method, in the estimation of discrepancies of hidden states, and in the identification of the sources (namely, model form errors) of the discrepancies. We adopt a partitioned approach to estimate discrepancies in weakly coupled systems and perform state estimation separately for the individual disciplines. The proposed approach is illustrated for a four-discipline problem related to aero-thermo-elastic response prediction of a hypersonic aircraft panel.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multi-disciplinary analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hypersonic aircraft panel</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Model form error</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Discrepancy estimation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Bayesian state estimation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mahadevan, Sankaran</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Miranda, Regina ELSEVIER</subfield><subfield code="t">Future-oriented repetitive thought, depressive symptoms, and suicide ideation severity: Role of future-event fluency and depressive predictive certainty</subfield><subfield code="d">2023</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV010178430</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:395</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:15</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:19-37</subfield><subfield code="g">extent:19</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jcp.2019.06.013</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.91</subfield><subfield code="j">Psychiatrie</subfield><subfield code="j">Psychopathologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">395</subfield><subfield code="j">2019</subfield><subfield code="b">15</subfield><subfield code="c">1015</subfield><subfield code="h">19-37</subfield><subfield code="g">19</subfield></datafield></record></collection>
|
| score |
7.400549 |