mlsanityData to 7 Oct 2026

Accounting for plasticity: An extension of inelastic Constitutive Artificial Neural Networks

arXiv 2407.19326 ↗First posted 27 Jul 2024cs.LGcited by 6

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Abstract (arXiv)

In this work, we extend the existing framework of inelastic constitutive artificial neural networks (iCANNs) by incorporating plasticity to increase their applicability to model more complex material behavior. The proposed approach ensures objectivity, material symmetry, and thermodynamic consistency, providing a robust basis for automatic model discovery of constitutive equations at finite strains. These are predicted by discovering formulations for the Helmholtz free energy and plastic potentials for the yield function and evolution equations in terms of feed-forward networks. Our framework captures both linear and nonlinear kinematic hardening behavior. Investigation of our model's prediction showed that the extended iCANNs successfully predict both linear and nonlinear kinematic hardening behavior based on experimental and artificially generated datasets, showcasing promising capabilities of this framework. Nonetheless, challenges remain in discovering more complex yield criteria with tension-compression asymmetry and addressing deviations in experimental data at larger strains. Despite these limitations, the proposed framework provides a promising basis for incorporating plasticity into iCANNs, offering a platform for advancing in the field of automated model discovery.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 26 of its 152 references that are in the map. First posted on arXiv 27 Jul 2024.

Builds on

26 of 152 references.

References · 26

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14 more
  1. 2021Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)cited by 18
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

  1. 2024Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodelingcited by 6
  2. 2026Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material modelscited by 5
  3. 2025A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strainscited by 4
  4. 2026A Convex Route to Thermoelasticity: Learning Internal Energy and Dissipationcited by 1
  5. 2026Discovering Thermodynamically Admissible Dissipation Potentials via Grammar-Based Symbolic Regressioncited by 0
  6. 2026Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation datacited by 0