Machine learning aided multiscale mechanics of fiber suspensions
We present a Fast-Fourier-Transform (FFT) based computational approach to computing the viscous stress response of rigid fibers suspended in a non-Newtonian medium. We identify closed-form models for the fiber suspension viscosity from data obtained with the FFT-based computational approach by lever...
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| Format: | Online |
| Jezik: | engleski |
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KIT Scientific Publishing
2025
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| Online pristup: | ONIX_20251202T160246_9783731514213_32 |
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| _version_ | 1869525012921188352 |
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| author | Sterr, Benedikt |
| author_browse | Sterr, Benedikt |
| author_facet | Sterr, Benedikt |
| author_sort | Sterr, Benedikt |
| collection | Directory of Open Access Books |
| description | We present a Fast-Fourier-Transform (FFT) based computational approach to computing the viscous stress response of rigid fibers suspended in a non-Newtonian medium. We identify closed-form models for the fiber suspension viscosity from data obtained with the FFT-based computational approach by leveraging supervised machine learning techniques. Furthermore, we present a novel Deep Material Network architecture capable of treating suspensions of rigid particles with high computational efficiency. |
| format | Online |
| id | doab-20.500.12854ir-169824 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1698242025-12-03T05:14:52Z Machine learning aided multiscale mechanics of fiber suspensions Sterr, Benedikt Maschinelles Lernen Datengetriebene Modellierung Numerische Mikromechanik Fasersuspensionen Deep Material Networks Machine learning Data-driven modelling computational Micromechanics Fiber suspensions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering We present a Fast-Fourier-Transform (FFT) based computational approach to computing the viscous stress response of rigid fibers suspended in a non-Newtonian medium. We identify closed-form models for the fiber suspension viscosity from data obtained with the FFT-based computational approach by leveraging supervised machine learning techniques. Furthermore, we present a novel Deep Material Network architecture capable of treating suspensions of rigid particles with high computational efficiency. 2025-12-03T05:14:51Z 2025-12-03T05:14:51Z 2025-12-02T15:14:05Z 2025 book ONIX_20251202T160246_9783731514213_32 2192-693X (Online) https://library.oapen.org/handle/20.500.12657/108924 9783731514213 https://directory.doabooks.org/handle/20.500.12854/169824 eng Schriftenreihe Kontinuumsmechanik im Maschinenbau open access image/jpeg Attribution-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/108924/1/9783731514213.pdf KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000179536 10.5445/KSP/1000179536 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731514213 KIT Scientific Publishing 206 Karlsruhe, Germany open access |
| spellingShingle | Maschinelles Lernen Datengetriebene Modellierung Numerische Mikromechanik Fasersuspensionen Deep Material Networks Machine learning Data-driven modelling computational Micromechanics Fiber suspensions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering Sterr, Benedikt Machine learning aided multiscale mechanics of fiber suspensions |
| title | Machine learning aided multiscale mechanics of fiber suspensions |
| title_full | Machine learning aided multiscale mechanics of fiber suspensions |
| title_fullStr | Machine learning aided multiscale mechanics of fiber suspensions |
| title_full_unstemmed | Machine learning aided multiscale mechanics of fiber suspensions |
| title_short | Machine learning aided multiscale mechanics of fiber suspensions |
| title_sort | machine learning aided multiscale mechanics of fiber suspensions |
| topic | Maschinelles Lernen Datengetriebene Modellierung Numerische Mikromechanik Fasersuspensionen Deep Material Networks Machine learning Data-driven modelling computational Micromechanics Fiber suspensions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering |
| topic_facet | Maschinelles Lernen Datengetriebene Modellierung Numerische Mikromechanik Fasersuspensionen Deep Material Networks Machine learning Data-driven modelling computational Micromechanics Fiber suspensions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering |
| url | ONIX_20251202T160246_9783731514213_32 |
| work_keys_str_mv | AT sterrbenedikt machinelearningaidedmultiscalemechanicsoffibersuspensions |