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Publisher DOI: 10.1002/pamm.202000284
Title: Concentration‐specific constitutive modeling of gelatin based on artificial neural networks
Language: English
Authors: Abdolazizi, Kian Philipp 
Linka, Kevin 
Sprenger, Johanna 
Neidhardt, Maximilian 
Schlaefer, Alexander 
Cyron, Christian J. 
Issue Date: 25-Jan-2021
Publisher: Wiley-VCH
Source: Proceedings in applied mathematics and mechanics 20 (1): 202000284 (2021)
Abstract (english): 
Gelatin phantoms are frequently used in the development of surgical devices and medical imaging techniques. They exhibit mechanical properties similar to soft biological tissues [1] but can be handled at a much lower cost. Moreover, they enable a better reproducibility of experiments. Accurate constitutive models for gelatin are therefore of great interest for biomedical engineering. In particular it is important to capture the dependence of mechanical properties of gelatin on its concentration. Herein we propose a simple machine learning approach to this end. It uses artificial neural networks (ANN) for learning from indentation data the relation between the concentration of ballistic gelatin and the resulting mechanical properties.
© 2021 The Authors Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH
Conference: 91st Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2021) 
DOI: 10.15480/882.3897
ISSN: 1617-7061
Journal: Proceedings in applied mathematics and mechanics 
Institute: Kontinuums- und Werkstoffmechanik M-15 
Medizintechnische und Intelligente Systeme E-1 
Document Type: Chapter/Article (Proceedings)
Project: I³-Lab - Modell-gestütztes maschinelles Lernen für die Weichgewebsmodellierung in der Medizin 
Projekt DEAL 
More Funding information: The authors greatfully acknowledge financial support from Hamburg University of Technology (TUHH) within the I3-Lab ‘Modell-gestütztes maschinelles Lernen für die Weichgewebsmodellierung in der Medizin’.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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