Department of Structural Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
J Biomech. 2021 Mar 5;117:110124. doi: 10.1016/j.jbiomech.2020.110124. Epub 2020 Nov 13.
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.
数据驱动建模直接利用机器学习技术和实验数据来预测材料的响应，而无需使用唯象本构模型。尽管数据驱动建模提出了一种很有前途的新方法，但它尚未扩展到大变形生物组织的建模中。在此，我们将最近的局部凸性数据驱动（LCDD）框架（He 和 Chen，2020）扩展到猪心二尖瓣后叶的力学响应建模中。通过使用各种双轴和纯剪切训练方案的组合来研究 LCDD 框架的可预测性，并将其有效性与来自 Zhang 等人（2016）的经过修改的完整结构、唯象模型和 Tong 和 Fung（1976）的连续唯象 Fung 型模型进行比较。结果表明，所提出的 LCDD 非线性求解器的可预测性通常对数据集中使用的加载方案（双轴和纯剪切）类型不太敏感，而与两个选定的唯象模型的可预测性相比，对实验数据的覆盖不足更为敏感。虽然在 LCDD 中不需要材料模型中的预定义函数形式，但本研究再次强调了在数据驱动和机器学习类型的方法中具有足够丰富的数据覆盖的重要性。结果还表明，与早期的距离最小化数据驱动（DMDD）方法相比，所提出的 LCDD 方法在处理噪声数据方面具有优越性。本研究表明，当有足够的数据可用时，数据驱动计算可以成为建模复杂生物材料的替代方法。
