Fernández, Mauricio ; Fritzen, Felix ; Weeger, Oliver (2022)
Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials.
In: International Journal for Numerical Methods in Engineering, 123 (2)
doi: 10.1002/nme.6869
Article, Bibliographie
This is the latest version of this item.
| Item Type: | Article |
|---|---|
| Erschienen: | 2022 |
| Creators: | Fernández, Mauricio ; Fritzen, Felix ; Weeger, Oliver |
| Type of entry: | Bibliographie |
| Title: | Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials |
| Language: | English |
| Date: | 30 January 2022 |
| Publisher: | Wiley |
| Journal or Publication Title: | International Journal for Numerical Methods in Engineering |
| Volume of the journal: | 123 |
| Issue Number: | 2 |
| DOI: | 10.1002/nme.6869 |
| Corresponding Links: | |
| Additional Information: | First published online: 01 November 2021 |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Cyber-Physical Simulation (CPS) |
| Date Deposited: | 01 Feb 2022 06:31 |
| Last Modified: | 03 Jul 2024 02:55 |
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| Suche nach Titel in: | TUfind oder in Google |
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Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials. (deposited 07 Jan 2022 14:06)
- Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials. (deposited 01 Feb 2022 06:31) [Currently Displayed]
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