TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publications
  4. Democratizing biomedical simulation through automated model discovery and a universal material subroutine
 
Options

Democratizing biomedical simulation through automated model discovery and a universal material subroutine

Citation Link: https://doi.org/10.15480/882.13760
Publikationstyp
Journal Article
Date Issued
2024
Sprache
English
Author(s)
Peirlinck, Mathias  
Linka, Kevin  
Kontinuums- und Werkstoffmechanik M-15  
Hurtado, Juan A.
Holzapfel, Gerhard A.  
Kuhl, Ellen  
TORE-DOI
10.15480/882.13760
TORE-URI
https://hdl.handle.net/11420/52181
Journal
Computational mechanics  
Volume
75
Issue
6
Start Page
1703
End Page
1723
Citation
Computational Mechanics 75 (6): 1703–1723 (2025)
Publisher DOI
10.1007/s00466-024-02515-y
Scopus ID
2-s2.0-85201195046
Publisher
Springer
Personalized computational simulations have emerged as a vital tool to understand the biomechanical factors of a disease, predict disease progression, and design personalized intervention. Material modeling is critical for realistic biomedical simulations, and poor model selection can have life-threatening consequences for the patient. However, selecting the best model requires a profound domain knowledge and is limited to a few highly specialized experts in the field. Here we explore the feasibility of eliminating user involvement and automate the process of material modeling in finite element analyses. We leverage recent developments in constitutive neural networks, machine learning, and artificial intelligence to discover the best constitutive model from thousands of possible combinations of a few functional building blocks. We integrate all discoverable models into the finite element workflow by creating a universal material subroutine that contains more than 60,000 models, made up of 16 individual terms. We prototype this workflow using biaxial extension tests from healthy human arteries as input and stress and stretch profiles across the human aortic arch as output. Our results suggest that constitutive neural networks can robustly discover various flavors of arterial models from data, feed these models directly into a finite element simulation, and predict stress and strain profiles that compare favorably to the classical Holzapfel model. Replacing dozens of individual material subroutines by a single universal material subroutine—populated directly via automated model discovery—will make finite element simulations more user-friendly, more robust, and less vulnerable to human error. Democratizing finite element simulation by automating model selection could induce a paradigm shift in physics-based modeling, broaden access to simulation technologies, and empower individuals with varying levels of expertise and diverse backgrounds to actively participate in scientific discovery and push the boundaries of biomedical simulation.
Subjects
Arteries | Cardiovascular mechanics | Constitutive neural networks | Hyperelasticity | Machine learning
DDC Class
620: Engineering
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
Loading...
Thumbnail Image
Name

s00466-024-02515-y.pdf

Size

4.31 MB

Format

Adobe PDF

TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback