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A data-driven inelasticity framework enhanced by neural network based propagators : Application to trusses

Harnisch, Marius ; Bartel, Thorsten and Menzel, Andreas LU (2026) In Computer Methods in Applied Mechanics and Engineering 453. p.118806-118806
Abstract
Research in computational mechanics is increasingly in search of methods that directly incorporate the information governed by data. Within these data-based methods, the data-driven mechanics approach seeks to replace conventional material models with discrete data sets. Offering immense potential in computational mechanics, where the simulation of complex mechanical systems is often challenging, the extension of the method to path-dependent material behavior is a crucial step. We achieved this extension by introducing history surrogates and an accompanying propagator which acts as an update rule. Finding suitable choices of these quantities can become challenging if they are selected by intuition. In the present paper, we explore the idea... (More)
Research in computational mechanics is increasingly in search of methods that directly incorporate the information governed by data. Within these data-based methods, the data-driven mechanics approach seeks to replace conventional material models with discrete data sets. Offering immense potential in computational mechanics, where the simulation of complex mechanical systems is often challenging, the extension of the method to path-dependent material behavior is a crucial step. We achieved this extension by introducing history surrogates and an accompanying propagator which acts as an update rule. Finding suitable choices of these quantities can become challenging if they are selected by intuition. In the present paper, we explore the idea of utilizing a Long Short-Term Memory (LSTM) neural network as propagator, which automatically calculates and updates the history surrogate. In this context, the network is not deployed in order to represent the underlying material behavior by replacing the data, but rather to assist the data-driven solver, which continues to calculate the stress-strain prediction in accordance with the considered boundary value problem and thereby enforces equilibrium of forces and kinematic compatibility. We introduce the framework required to achieve accurate results and provide a thorough investigation of this framework, covering data generation, network architecture and network training. We demonstrate the capabilities of the LSTM enhancement for the example of plasticity with isotropic hardening and provide a comparison to a framework without the enhancement highlighting the improvements. Most notably, the proposed framework operates fully automated, without requiring user input, while maintaining the high accuracy of the approach without the improvements.
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publication status
published
subject
in
Computer Methods in Applied Mechanics and Engineering
volume
453
pages
118806 - 118806
publisher
Elsevier
ISSN
0045-7825
DOI
10.1016/j.cma.2026.118806
language
English
LU publication?
yes
id
3a9bbb7f-f900-4ccb-bd74-5d8b7a35281f
date added to LUP
2026-02-17 13:39:59
date last changed
2026-02-23 12:05:46
@article{3a9bbb7f-f900-4ccb-bd74-5d8b7a35281f,
  abstract     = {{Research in computational mechanics is increasingly in search of methods that directly incorporate the information governed by data. Within these data-based methods, the data-driven mechanics approach seeks to replace conventional material models with discrete data sets. Offering immense potential in computational mechanics, where the simulation of complex mechanical systems is often challenging, the extension of the method to path-dependent material behavior is a crucial step. We achieved this extension by introducing history surrogates and an accompanying propagator which acts as an update rule. Finding suitable choices of these quantities can become challenging if they are selected by intuition. In the present paper, we explore the idea of utilizing a Long Short-Term Memory (LSTM) neural network as propagator, which automatically calculates and updates the history surrogate. In this context, the network is not deployed in order to represent the underlying material behavior by replacing the data, but rather to assist the data-driven solver, which continues to calculate the stress-strain prediction in accordance with the considered boundary value problem and thereby enforces equilibrium of forces and kinematic compatibility. We introduce the framework required to achieve accurate results and provide a thorough investigation of this framework, covering data generation, network architecture and network training. We demonstrate the capabilities of the LSTM enhancement for the example of plasticity with isotropic hardening and provide a comparison to a framework without the enhancement highlighting the improvements. Most notably, the proposed framework operates fully automated, without requiring user input, while maintaining the high accuracy of the approach without the improvements.<br/>}},
  author       = {{Harnisch, Marius and Bartel, Thorsten and Menzel, Andreas}},
  issn         = {{0045-7825}},
  language     = {{eng}},
  month        = {{05}},
  pages        = {{118806--118806}},
  publisher    = {{Elsevier}},
  series       = {{Computer Methods in Applied Mechanics and Engineering}},
  title        = {{A data-driven inelasticity framework enhanced by neural network based propagators : Application to trusses}},
  url          = {{http://dx.doi.org/10.1016/j.cma.2026.118806}},
  doi          = {{10.1016/j.cma.2026.118806}},
  volume       = {{453}},
  year         = {{2026}},
}