Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with...
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| Natura: | Online |
| Lingua: | inglese |
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KIT Scientific Publishing
2023
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| Soggetti: | |
| Accesso online: | OCN: 1402511918 |
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| _version_ | 1869520711085719552 |
|---|---|
| author | Gajek, Sebastian |
| author_browse | Gajek, Sebastian |
| author_facet | Gajek, Sebastian |
| author_sort | Gajek, Sebastian |
| collection | Directory of Open Access Books |
| description | We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations. |
| format | Online |
| id | doab-20.500.12854ir-121498 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1214982025-05-27T05:56:27Z Deep material networks for efficient scale-bridging in thermomechanical simulations of solids Gajek, Sebastian deep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernen We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations. 2023-11-16T11:17:59Z 2023-11-16T11:17:59Z 2023-09-04T12:19:03Z 2023 book OCN: 1402511918 https://library.oapen.org/handle/20.500.12657/76126 9783731512783 https://directory.doabooks.org/handle/20.500.12854/121498 eng Schriftenreihe Kontinuumsmechanik im Maschinenbau open access image/jpeg image/jpeg image/jpeg image/jpeg image/jpeg image/jpeg Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/76126/1/deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf https://library.oapen.org/bitstream/20.500.12657/76126/1/deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf https://library.oapen.org/bitstream/20.500.12657/76126/1/deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf https://library.oapen.org/bitstream/20.500.12657/76126/1/deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf https://library.oapen.org/bitstream/20.500.12657/76126/1/deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf https://library.oapen.org/bitstream/20.500.12657/76126/1/deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf KIT Scientific Publishing 10.5445/KSP/1000155688 10.5445/KSP/1000155688 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731512783 AG Universitätsverlage 326 open access |
| spellingShingle | deep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernen Gajek, Sebastian Deep material networks for efficient scale-bridging in thermomechanical simulations of solids |
| title | Deep material networks for efficient scale-bridging in thermomechanical simulations of solids |
| title_full | Deep material networks for efficient scale-bridging in thermomechanical simulations of solids |
| title_fullStr | Deep material networks for efficient scale-bridging in thermomechanical simulations of solids |
| title_full_unstemmed | Deep material networks for efficient scale-bridging in thermomechanical simulations of solids |
| title_short | Deep material networks for efficient scale-bridging in thermomechanical simulations of solids |
| title_sort | deep material networks for efficient scale bridging in thermomechanical simulations of solids |
| topic | deep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernen |
| topic_facet | deep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernen |
| url | OCN: 1402511918 |
| work_keys_str_mv | AT gajeksebastian deepmaterialnetworksforefficientscalebridginginthermomechanicalsimulationsofsolids |