Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK.
Methods. 2021 Jan;185:105-109. doi: 10.1016/j.ymeth.2020.02.004. Epub 2020 Feb 6.
In computational modelling of musculoskeletal applications, one of the critical aspects is ensuring that a model can capture intrinsic population variability and not only representative of a "mean" individual. Developing and calibrating models with this aspect in mind is key for the credibility of a modelling methodology. This often requires calibration of complex models with respect to 3D experiments and measurements on a range of specimens or patients. Most Finite Element (FE) software's do not have such a capacity embedded in their core tools. This paper presents a versatile interface between Finite Element (FE) software and optimisation tools, enabling calibration of a group of FE models on a range of experimental data. It is provided as a Python toolbox which has been fully tested and verified on Windows platforms. The toolbox is tested in three case studies involving in vitro testing of spinal tissues.
在肌肉骨骼应用的计算建模中，至关重要的一个方面是确保模型能够捕捉内在的群体变异性，而不仅仅是代表“平均”个体。考虑到这一方面来开发和校准模型是建模方法可信度的关键。这通常需要根据 3D 实验和一系列标本或患者的测量结果来校准复杂的模型。大多数有限元 (FE) 软件的核心工具都不具备这种功能。本文提出了一种在有限元 (FE) 软件和优化工具之间的通用接口，能够根据一系列实验数据对一组 FE 模型进行校准。它作为一个 Python 工具箱提供，已经在 Windows 平台上进行了全面测试和验证。该工具箱在三个案例研究中进行了测试，涉及脊柱组织的体外测试。
