Layer-wise relevance propagation for backbone identification in discrete fracture networks
In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to re...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Berrone, Stefano [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Effective mineralization of organic dye under visible-light irradiation over electronic-structure-modulated Sn(Nb1−x Ta x )2O6 solid solutions - Ren, Jian ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:55 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jocs.2021.101458 |
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| 520 | |a In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to reduce the computational cost of simulations. With a suitably trained Neural Network at hand, we use the Layer-wise Relevance Propagation as a feature selection method to detect the expected relevance of each fracture in a Discrete Fracture Network and thus identifying the backbone. | ||
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10.1016/j.jocs.2021.101458 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001609.pica (DE-627)ELV05593840X (ELSEVIER)S1877-7503(21)00135-6 DE-627 ger DE-627 rakwb eng 540 VZ 570 VZ BIODIV DE-30 fid PHARM DE-84 fid 44.00 bkl Berrone, Stefano verfasserin aut Layer-wise relevance propagation for backbone identification in discrete fracture networks 2021 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to reduce the computational cost of simulations. With a suitably trained Neural Network at hand, we use the Layer-wise Relevance Propagation as a feature selection method to detect the expected relevance of each fracture in a Discrete Fracture Network and thus identifying the backbone. 65D40 Elsevier 68T37 Elsevier 68T07 Elsevier 76-10 Elsevier 76-11 Elsevier Della Santa, Francesco oth Mastropietro, Antonio oth Pieraccini, Sandra oth Vaccarino, Francesco oth Enthalten in Elsevier Ren, Jian ELSEVIER Effective mineralization of organic dye under visible-light irradiation over electronic-structure-modulated Sn(Nb1−x Ta x )2O6 solid solutions 2015 Amsterdam [u.a.] (DE-627)ELV018619908 volume:55 year:2021 pages:0 https://doi.org/10.1016/j.jocs.2021.101458 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_40 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2011 44.00 Medizin: Allgemeines VZ AR 55 2021 0 |
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10.1016/j.jocs.2021.101458 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001609.pica (DE-627)ELV05593840X (ELSEVIER)S1877-7503(21)00135-6 DE-627 ger DE-627 rakwb eng 540 VZ 570 VZ BIODIV DE-30 fid PHARM DE-84 fid 44.00 bkl Berrone, Stefano verfasserin aut Layer-wise relevance propagation for backbone identification in discrete fracture networks 2021 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to reduce the computational cost of simulations. With a suitably trained Neural Network at hand, we use the Layer-wise Relevance Propagation as a feature selection method to detect the expected relevance of each fracture in a Discrete Fracture Network and thus identifying the backbone. 65D40 Elsevier 68T37 Elsevier 68T07 Elsevier 76-10 Elsevier 76-11 Elsevier Della Santa, Francesco oth Mastropietro, Antonio oth Pieraccini, Sandra oth Vaccarino, Francesco oth Enthalten in Elsevier Ren, Jian ELSEVIER Effective mineralization of organic dye under visible-light irradiation over electronic-structure-modulated Sn(Nb1−x Ta x )2O6 solid solutions 2015 Amsterdam [u.a.] (DE-627)ELV018619908 volume:55 year:2021 pages:0 https://doi.org/10.1016/j.jocs.2021.101458 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_40 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2011 44.00 Medizin: Allgemeines VZ AR 55 2021 0 |
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Effective mineralization of organic dye under visible-light irradiation over electronic-structure-modulated Sn(Nb1−x Ta x )2O6 solid solutions |
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Effective mineralization of organic dye under visible-light irradiation over electronic-structure-modulated Sn(Nb1−x Ta x )2O6 solid solutions |
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2021 |
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Berrone, Stefano |
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Elektronische Aufsätze |
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Berrone, Stefano |
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10.1016/j.jocs.2021.101458 |
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540 570 |
| title_sort |
layer-wise relevance propagation for backbone identification in discrete fracture networks |
| title_auth |
Layer-wise relevance propagation for backbone identification in discrete fracture networks |
| abstract |
In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to reduce the computational cost of simulations. With a suitably trained Neural Network at hand, we use the Layer-wise Relevance Propagation as a feature selection method to detect the expected relevance of each fracture in a Discrete Fracture Network and thus identifying the backbone. |
| abstractGer |
In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to reduce the computational cost of simulations. With a suitably trained Neural Network at hand, we use the Layer-wise Relevance Propagation as a feature selection method to detect the expected relevance of each fracture in a Discrete Fracture Network and thus identifying the backbone. |
| abstract_unstemmed |
In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to reduce the computational cost of simulations. With a suitably trained Neural Network at hand, we use the Layer-wise Relevance Propagation as a feature selection method to detect the expected relevance of each fracture in a Discrete Fracture Network and thus identifying the backbone. |
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| title_short |
Layer-wise relevance propagation for backbone identification in discrete fracture networks |
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https://doi.org/10.1016/j.jocs.2021.101458 |
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Della Santa, Francesco Mastropietro, Antonio Pieraccini, Sandra Vaccarino, Francesco |
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Della Santa, Francesco Mastropietro, Antonio Pieraccini, Sandra Vaccarino, Francesco |
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10.1016/j.jocs.2021.101458 |
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