Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Current Trends and Open Problems in Computational Mechanics |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer International Publishing AG |
| Seiten | 569-579 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 9783030873127 |
| ISBN (Print) | 9783030873110 |
| Publikationsstatus | Veröffentlicht - 13 März 2022 |
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.
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Current Trends and Open Problems in Computational Mechanics. Cham: Springer International Publishing AG, 2022. S. 569-579.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Computational Homogenization Using Convolutional Neural Networks
AU - Wessels, Henning
AU - Böhm, Christoph
AU - Aldakheel, Fadi
AU - Hüpgen, Markus
AU - Haist, Michael
AU - Lohaus, Ludger
AU - Wriggers, Peter
N1 - FA, MH, MH, LL and PW acknowledge funding through the DFG Priority Program SPP 2020 Experimental-Virtual-Lab under the grants number 373757395; project WR 19/58-2 and GZ-LO 751/22-2 (668132). CB, FA and PW thank the German Research Foundation (DFG) for financial support to this work in the Collaborative Research Center SFB 1153 Process chain for the production of hybrid high-performance components through tailored forming with the subproject C04 Modelling and simulation of the joining zone, project number 252662854.
PY - 2022/3/13
Y1 - 2022/3/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85131539081&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87312-7_55
DO - 10.1007/978-3-030-87312-7_55
M3 - Contribution to book/anthology
AN - SCOPUS:85131539081
SN - 9783030873110
SP - 569
EP - 579
BT - Current Trends and Open Problems in Computational Mechanics
PB - Springer International Publishing AG
CY - Cham
ER -