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February 21, 2026Computational Mechanics0 citationsOpen Access

Effective reduced-order approximation for fast and robust MCRE-based parametric identification of nonlinear history-dependent material laws

MBMainak BhattacharyyaLCL. Chamoin

Key Points

  • The aim is to enhance the efficiency of model updating procedures for nonlinear materials using reduced-order modeling techniques.
  • Introduced a generalised decomposition based formulation separating space and time problems.
  • Developed a proper orthogonal decomposition method for model reduction of variable Hooke’s tensor.
  • Conducted several numerical tests to validate the proposed methodologies.
  • Achieved low fidelity approximations for elasto(visco)-plasticity and damage models.
  • Demonstrated improved numerical efficiency in parametric identification processes.

Abstract

Abstract This article essentially addresses the numerical frugality of model updating procedures using reduced order modelling. Nonlinear material behaviour is tackled in the article with the focus being on elasto(visco)-plasticity and elasto(visco)-plastic-damage. A proper generalised decomposition based formulation is introduced that separates the governing equations into sundered space and time problems, thereby providing low fidelity approximations. A proper orthogonal decomposition based model reduction method is also introduced to tackle variable Hooke’s tensor for softening material behaviour. The proposed methodologies are exemplified through several numerical tests in order to assess and validate performance.

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Cite This Study

Bhattacharyya et al. (2026) studied this question.

synapsesocial.com/papers/69994c6f873532290d020efbhttps://doi.org/10.1007/s00466-026-02754-1
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