Towards a data-driven paradigm for characterizing plastic anisotropy using principal components analysis and manifold learning - Université de technologie de Compiègne
Journal Articles Computational Materials Science Year : 2024

Towards a data-driven paradigm for characterizing plastic anisotropy using principal components analysis and manifold learning

Jianqiang Jin
Roberval
Ludovic Cauvin
Roberval
CV
Balaji Raghavan
Laboratoire de Génie Civil et Génie Mécanique
CV
Piotr Breitkopf
  • Function : Author
Roberval
Subhrajit Dutta
  • Function : Author
National Institute of Technology [Silchar]
Manyu Xiao
  • Function : Author
Northernwest Polytechnical University [Xi'an]

Domains

Mechanics [physics.med-ph] Mechanics of materials [physics.class-ph] Machine Learning [stat.ML]

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https://utc.hal.science/hal-04445491

Submitted on : Thursday, February 8, 2024-12:08:28 AM

Last modification on : Sunday, November 17, 2024-10:00:02 PM

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hal-04445491 , version 1 (08-02-2024)

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Jianqiang Jin, Ludovic Cauvin, Balaji Raghavan, Piotr Breitkopf, Subhrajit Dutta, et al.. Towards a data-driven paradigm for characterizing plastic anisotropy using principal components analysis and manifold learning. Computational Materials Science, 2024, 235, pp.112834. ⟨10.1016/j.commatsci.2024.112834⟩. ⟨hal-04445491⟩

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