Manifold learning based data-driven modeling for soft biological tissues

Qizhi He, Devin W. Laurence, Chung Hao Lee, Jiun Shyan Chen

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Data-driven modeling directly utilizes experimental data with machine learning techniques to predict a material's response without the necessity of using phenomenological constitutive models. Although data-driven modeling presents a promising new approach, it has yet to be extended to the modeling of large-deformation biological tissues. Herein, we extend our recent local convexity data-driven (LCDD) framework (He and Chen, 2020) to model the mechanical response of a porcine heart mitral valve posterior leaflet. The predictability of the LCDD framework by using various combinations of biaxial and pure shear training protocols are investigated, and its effectiveness is compared with a full structural, phenomenological model modified from Zhang et al. (2016) and a continuum phenomenological Fung-type model (Tong and Fung, 1976). We show that the predictivity of the proposed LCDD nonlinear solver is generally less sensitive to the type of loading protocols (biaxial and pure shear) used in the data set, while more sensitive to the insufficient coverage of the experimental data when compared to the predictivity of the two selected phenomenological models. While no pre-defined functional form in the material model is necessary in LCDD, this study reinstates the importance of having sufficiently rich data coverage in the date-driven and machine learning type of approaches. It is also shown that the proposed LCDD method is an enhancement over the earlier distance-minimization data-driven (DMDD) against noisy data. This study demonstrates that when sufficient data is available, data-driven computing can be an alternative method for modeling complex biological materials.

Original languageEnglish (US)
Article number110124
JournalJournal of Biomechanics
StatePublished - Mar 5 2021
Externally publishedYes

Bibliographical note

Funding Information:
Supports from the American Heart Association Scientist Development Grant Award (16SDG27760143) and the Presbyterian Health Foundation Team Science Grant (C5122401) to the 2 nd and 3 rd author from The University of Oklahoma (OU), and the National Institute of Health under grant number 1 R01 AG056999-01A1 to the 1 st and 4 th authors from the University of California, San Diego are gratefully acknowledged. CHL (3 rd author) was in part supported by the institutional start-up funds from the School of Aerospace and Mechanical Engineering (AME), the IBEST-OUHSC Funding for Interdisciplinary Research, and the research funding through the Faculty Investment Program from the Research Council at OU. DWL (2 nd author) was supported by the National Science Foundation Graduate Research Fellowship (NSF GRFP 2019254233).

Publisher Copyright:
© 2020 Elsevier Ltd


  • Data-driven material modeling
  • Hyperelasticity
  • Local convexity data-driven method
  • Manifold learning
  • Mitral heart valve


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