Multi-field decomposed hyper-reduced order modeling of damage-plasticity simulations

This paper presents a multi-field decomposed approach for hyper-reduced order modeling to overcome the limitations of traditional model reduction techniques for gradient-extended damage-plasticity simulations. The discrete empirical interpolation method (DEIM) and the energy-conserving sampling and weighting method (ECSW) are extended to account for the multi-field nature of the problem. Both methods show better stability when compared to their unmodified versions. Accurate results can be achieved while significantly reducing the computational cost compared to full-order simulations. Two numerical examples are presented to demonstrate the performance and limitations of the proposed approaches. The decomposed ECSW method has overall higher accuracy and lower computational cost than the decomposed DEIM method.

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Publication Details

Journal
Computer Methods in Applied Mechanics and Engineering
Published
2026-01-05
DOI
https://doi.org/10.1016/j.cma.2025.118702
Citations
1
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
36.90
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article

Multi-field decomposed hyper-reduced order modeling of damage-plasticity simulations

Stefanie Reese, Tim Brepols, Stephan Ritzert, Jannick Kehls et al.
1 citations
Computer Methods in Applied Mechanics and Engineering
Model Reduction and Neural Networks
36.90
article

Multi-field decomposed hyper-reduced order modeling of damage-plasticity simulations

Stefanie Reese, Tim Brepols, Stephan Ritzert, Jannick Kehls, Lars Breuer, Qinghua Zhang
article en
1 citations

Abstract

This paper presents a multi-field decomposed approach for hyper-reduced order modeling to overcome the limitations of traditional model reduction techniques for gradient-extended damage-plasticity simulations. The discrete empirical interpolation method (DEIM) and the energy-conserving sampling and weighting method (ECSW) are extended to account for the multi-field nature of the problem. Both methods show better stability when compared to their unmodified versions. Accurate results can be achieved while significantly reducing the computational cost compared to full-order simulations. Two numerical examples are presented to demonstrate the performance and limitations of the proposed approaches. The decomposed ECSW method has overall higher accuracy and lower computational cost than the decomposed DEIM method.

Computer Methods in Applied Mechanics and EngineeringVol. 451
University of Siegen (DE), RWTH Aachen University (DE)
Affordable and clean energy
Openalex Percentile: Top 2%
Model Reduction and Neural Networks
36.90
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Multi-field decomposed hyper-reduced order modeling of damage-plasticity simulations — Stefanie Reese, Tim Brepols, et al. · Computer Methods in Applied Mechanics and Engineering (2026) | TGRS Research Map | TGRS