ArticleJournal of biomechanics2021
Manifold learning based data-driven modeling for soft biological tissues.
Article in Journal of biomechanics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Disentangled Representation Learning for Parametric Partial Differential Equations.... International Conference on Learning Representations · 2026Article
- HETEROGENEOUS PERIDYNAMIC NEURAL OPERATORS: DISCOVER BIOTISSUE CONSTITUTIVE LAW AND MICROSTRUCTURE FROM DIGITAL IMAGE CORRELATION MEASUREMENTS.Foundations of data science (Springfield, Mo.) · 2025Article
- FEBio FINESSE: An Open-Source Finite Element Simulation Approach to Estimate In Vivo Heart Valve Strains Using Shape Enforcement.Annals of biomedical engineering · 2025Article
- Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery.Advances in neural information processing systems · 2024Article
- A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems.Computational mechanics · 2024Article
- MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics.Computer methods in applied mechanics and engineering · 2023Article
- Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues.Computer methods in applied mechanics and engineering · 2023Article
- A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements.Journal of biomechanical engineering · 2022Article
- A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems.Journal of biomechanical engineering · 2022Article
- Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue.Engineering with computers · 2022Article
- Ex vivo experimental characterizations for understanding the interrelationship between tissue mechanics and collagen microstructure of porcine mitral valve leaflets.Journal of the mechanical behavior of biomedical materials · 2022Article
- Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.Annals of biomedical engineering · 2022Review
- Linking collagen fiber architecture to tissue-level biaxial mechanical behaviors of porcine semilunar heart valve cusps.Journal of the mechanical behavior of biomedical materials · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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