Computational Homogenization Using Convolutional Neural Networks
- verfasst von
- Henning Wessels, Christoph Böhm, Fadi Aldakheel, Markus Hüpgen, Michael Haist, Ludger Lohaus, Peter Wriggers
- Abstract
The classic tasks of computational engineers are to investigate and optimize structures in terms of their mechanical behavior. This iterative process usually requires a large number of calculations of different macroscopic structures of the same material. The computational time in this design-loop directly affects the time to market. Depending on the model complexity, describing the interaction between micro- and macro-scale can be computationally expensive and even prohibitive for engineering practice. This holds especially true if the physics on the micro-scale is complex involving inelastic behavior, fracture and/or phase change. In this paper, recent trends in Scientific Machine Learning (SciML), which may advance computational homogenization in the sense of the digital twin paradigm, are reviewed. We believe that SciML techniques for computational homogenization will make micro-macro simulations become applicable at lowextra cost in engineering practice. This work is partially funded by the DFG Priority Program SPP 2020 Experimental- Virtual-Lab and the DFG Collaborative Research Center SFB 1153 Tailored Forming.
- Organisationseinheit(en)
-
Institut für Kontinuumsmechanik
Institut für Baustoffe
- Externe Organisation(en)
-
Technische Universität Braunschweig
- Typ
- Beitrag in Buch/Sammelwerk
- Seiten
- 569-579
- Anzahl der Seiten
- 11
- Publikationsdatum
- 13.03.2022
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.), Informatik (insg.)
- Elektronische Version(en)
-
https://doi.org/10.1007/978-3-030-87312-7_55 (Zugang:
Geschlossen)
