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Cited by 47Elasticity and Material ModelingMachine Learning in Materials ScienceAdvanced Materials and Mechanics

Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data

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Leng, Yue, Tac, Vahidullah, Calve, Sarah, Tepole, Adrian B.·2021·Computer Methods in Applied Mechanics and Engineering
DOI 10.1016/j.cma.2021.114160
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  • Fibrin naturally assembles into gels and thrombus formation in vivo during wound healing and is an important component of the ECM.

    “Fibrin naturally assembles into gels and thrombus formation in vivo during wound healing and is an important component of the ECM in the body [64].”

    From Resorbable Biomaterials Used for 3D Scaffolds in Tissue Engineering: A Review (2023) · cites this paper as [64]

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Cited by

Sorted by citations

Authors
Leng, Y., Tac, V., Calve, S., & Tepole, A. B.
Affiliations
  • Purdue University West Lafayette (US)
  • University of Colorado Boulder (US)
ORCID
Venue
Computer Methods in Applied Mechanics and Engineering
Publisher
Elsevier BV
Published
2021-12
Volume
387
Pages
114160
ISSN
0045-7825 (ISSN-L)
DOI
10.1016/j.cma.2021.114160
OpenAlex
W3194035015
Cited by
47
References
1
Citations by year
2026: 8 · 2025: 12 · 2024: 10 · 2023: 8 · 2022: 8 · 2021: 1
FWCI
2.02
Type
journal article
Language
en
Open access
Licence
Locations
  • Computer Methods in Applied Mechanics and Engineering (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1911346-CMMI)
  • NSF CMMI
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Elasticity and Material Modeling
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
Keywords
biopolymer gels, fibrin, collagen, fiber networks, representative volume element, fully connected neural network, strain energy function, biaxial deformation, user material subroutine, ABAQUS, multiscale modeling, Gaussian process regression, tissue engineering

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