On the constraints and consistency in implicit constitutive modelling using ANNs and indirect training

On the constraints and consistency in implicit constitutive modelling using ANNs and indirect training


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Abstract. The training of an Artificial Neural Network (ANN) for implicit constitutive modelling mostly relies on labelled data pairs, however, some variables cannot be physically measured in real experiments. As such, the training should preferably be carried out indirectly, making use of experimentally measurable variables. The unconstrained training of an ANN’s parameters often leads to spurious responses that do not comply with the physics of the problem. Applying constraints during training ensures not only the physical meaning of the ANN predictions but also potentially increases the convergence to a global minimum, while improving the model’s performance. An ANN material model is trained using a novel indirect approach, where the local and global equilibrium conditions are ensured employing the Virtual Fields Method (VFM). An example of physical constraint is analyzed and applied during the training process.

Constitutive Model, Elastoplasticity, Neural Networks, Indirect Training, Constrained Optimization

Published online 4/19/2023, 12 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: LOURENÇO Rúben, CUETO Elías, GEORGIEVA Pétia, ANDRADE-CAMPOS António, On the constraints and consistency in implicit constitutive modelling using ANNs and indirect training, Materials Research Proceedings, Vol. 28, pp 1143-1154, 2023

DOI: https://doi.org/10.21741/9781644902479-125

The article was published as article 125 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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