MAP123-EP: A mechanistic-based data-driven approach for numerical elastoplastic analysis

Shan Tang, Ying Li, Hai Qiu, Hang Yang, Sourav Saha, Satyajit Mojumder, Wing Kam Liu*, Xu Guo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


In this paper, a mechanistic-based data-driven approach, MAP123-EP, is proposed for numerical analysis of elastoplastic materials. In this method, stress-update is driven by a set of one-dimensional stress–strain data generated by numerical or physical experiments under uniaxial loading. Numerical results indicate that combined with the classical strain-driven scheme, the proposed method can predict the mechanical response of isotropic elastoplastic materials (characterized by J2 plasticity model with isotropic/kinematic hardening and associated Drucker–Prager model) accurately without resorting to the typical ingredients of classical model-based plasticity, such as decomposing the total strain into elastic and plastic parts, as well as identifying explicit functional expressions of yielding surface and hardening curve. This mechanistic-based data-driven approach has the potential of opening up a new avenue for numerical analysis of problems where complex material behaviors cannot be described in explicit function/functional forms. The applicability and limitation of the proposed approach are also discussed.

Original languageEnglish (US)
Article number112955
JournalComputer Methods in Applied Mechanics and Engineering
StatePublished - Jun 1 2020


  • Constitutive law
  • Data-driven
  • Elastoplastic material
  • Finite element analysis
  • Strain-driven

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications


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