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  4. A mixture of experts approach to handle ambiguities in parameter identification problems in material modeling
 
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2019
Journal Article
Title

A mixture of experts approach to handle ambiguities in parameter identification problems in material modeling

Abstract
To simulate the mechanical behavior of a material, it is essential to calibrate the internal parameters of the used material model to experimental measurements. This is typically done in a trail-and-error approach by hand or automatically using optimization algorithms. As an alternative to trial-and-error, neural network-based approaches can be used to imitate the inverse mapping. This is usually realized in a grey-box model, combining neural networks, deterministic models, and domain knowledge. However, the proposed neural network-based approaches found in literature do not address the challenge that is posed when the parameter identification problem is non-unique. In the present paper this problem is discussed and an improved approach is proposed using a mixture of experts model. Mixture of experts is an ensemble technique based on a dynamically structured framework of submodels aiming to partition the non-unique problem into unique subtasks.
Author(s)
Morand, L.
Fraunhofer-Institut für Werkstoffmechanik IWM  
Helm, D.
Fraunhofer-Institut für Werkstoffmechanik IWM  
Journal
Computational materials science  
Open Access
File(s)
Download (1.93 MB)
DOI
10.24406/publica-r-257759
10.1016/j.commatsci.2019.04.003
Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Keyword(s)
  • grey-box model

  • direct inverse model

  • inverse problem

  • machine learning

  • material modeling

  • mixture of experts

  • neural network

  • parameter identification

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