| id |
oapen-20.500.12657-76126
|
| record_format |
dspace
|
| spelling |
oapen-20.500.12657-761262023-09-05T02:13:38Z 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 bic Book Industry Communication::T Technology, engineering, agriculture::TG Mechanical engineering & materials 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. 2023-09-04T12:19:03Z 2023-09-04T12:19:03Z 2023 book https://library.oapen.org/handle/20.500.12657/76126 eng Schriftenreihe Kontinuumsmechanik im Maschinenbau application/pdf Attribution-ShareAlike 4.0 International deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf https://doi.org/10.5445/KSP/1000155688 KIT Scientific Publishing 10.5445/KSP/1000155688 10.5445/KSP/1000155688 44e29711-8d53-496b-85cc-3d10c9469be9 26 326 open access
|
| institution |
OAPEN
|
| collection |
DSpace
|
| language |
English
|
| 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.
|
| title |
deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf
|
| spellingShingle |
deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf
|
| title_short |
deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf
|
| title_full |
deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf
|
| title_fullStr |
deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf
|
| title_full_unstemmed |
deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf
|
| title_sort |
deep-material-networks-for-efficient-scale-bridging-in-thermomechanical-simulations-of-solids.pdf
|
| publisher |
KIT Scientific Publishing
|
| publishDate |
2023
|
| url |
https://doi.org/10.5445/KSP/1000155688
|
| _version_ |
1799945222930563072
|
