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Learning data-driven reduced elastic and inelastic models of spot-welded patches
- Source :
- Mechanics & Industry, Mechanics & Industry, EDP Sciences, 2021, 22, pp.32. ⟨10.1051/meca/2021031⟩, Mechanics & Industry, Vol 22, p 32 (2021)
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- International audience; Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors.
- Subjects :
- Scale (ratio)
Computer science
Structure (category theory)
Stability (learning theory)
02 engineering and technology
Degrees of freedom (mechanics)
Sciences de l'ingénieur
01 natural sciences
Industrial and Manufacturing Engineering
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Machine Learning
0203 mechanical engineering
Artificial Intelligence
General Materials Science
Statistical physics
0101 mathematics
Spot welding
Materials of engineering and construction. Mechanics of materials
Spot-Welds
Model order reduction
Mechanical Engineering
Work (physics)
Data-Driven Mechanics
010101 applied mathematics
020303 mechanical engineering & transports
[SPI.MECA.STRU]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Structural mechanics [physics.class-ph]
TA401-492
Model Order Reduction
Element (category theory)
Subjects
Details
- Language :
- English
- ISSN :
- 22577777 and 22577750
- Database :
- OpenAIRE
- Journal :
- Mechanics & Industry, Mechanics & Industry, EDP Sciences, 2021, 22, pp.32. ⟨10.1051/meca/2021031⟩, Mechanics & Industry, Vol 22, p 32 (2021)
- Accession number :
- edsair.doi.dedup.....5a7f2c2a73af87c470c52d91fabc7ca9
- Full Text :
- https://doi.org/10.1051/meca/2021031⟩