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.

中文翻译：

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使用逆有限元分析来识别脊柱组织的原位行为


在肌肉骨骼应用的计算建模中，关键方面之一是确保模型能够捕获内在的群体变异性，而不仅仅是代表“平均”个体。考虑到这一点来开发和校准模型是建模方法可信度的关键。这通常需要针对一系列样本或患者的 3D 实验和测量来校准复杂模型。大多数有限元 (FE) 软件的核心工具中都没有嵌入这样的功能。本文提出了有限元 (FE) 软件和优化工具之间的通用接口，可以根据一系列实验数据校准一组 FE 模型。它作为 Python 工具箱提供，已在 Windows 平台上经过全面测试和验证。该工具箱在涉及脊柱组织体外测试的三个案例研究中进行了测试。