Adaptive Surrogate Modeling for Efficient Coupling of Musculoskeletal Control and Tissue Deformation Models
Adaptive Surrogate Modeling for Efficient Coupling of Musculoskeletal Control and Tissue Deformation Models
复制标题
DOI：
10.1115/1.3005333
复制
发表时间：
2009-01-01
影响因子：
1.7
通讯作者：
van den Bogert, Antonie J.
中科院分区：
文献类型：
--
作者：
Halloran, Jason P.;Erdemir, Ahmet;van den Bogert, Antonie J.
传统上，有限元（FE）建模和多体动力学分别应用于组织力学和肌肉骨骼运动领域。当感兴趣的是组织和运动之间的相互作用时，需要同时模拟两个域，但是由于高计算成本，这在很大程度上仍然是不切实际的。在这里，我们提出了一种方法，用于组织和运动的并发模拟，在每个域中使用的最先进的方法，并通过代理建模系统的基础上局部加权回归通信发生。代理模型仅在先前结果的回归不在用户指定的公差范围内时执行FE模拟。为了证明概念和说明可行性，该方法被证明对跳跃运动的优化，使用一个平面肌肉骨骼模型耦合到FE模型的脚。为了测试代理模型输出相对于FE模型输出的相对精度，在每个积分步骤中使用FE调用执行单个正向动力学模拟，并与包含代理模型的相应模拟进行比较。从跳跃高度优化中获得的神经激励用于此目的，并计算代理和FE模型输出（踝关节力和力矩、峰值接触压力和峰值von Mises应力）之间的均方根（RMS）差。跳跃高度的优化需要运动模拟的1800次迭代，每次需要数千个时间步。替代建模系统仅在5%的时间步长中使用FE模型，即，计算时间减少了95%由代理模型引入的误差在跳跃高度小于1 mm，地面反作用力的RMS误差小于2 N，踝关节力矩为0.25 Nm，峰值组织应力为10 kPa。基于局部回归的自适应代理建模允许有效地并行模拟组织力学和肌肉骨骼运动。
Finite element (FE) modeling and multibody dynamics have traditionally been applied separately to the domains of tissue mechanics and musculoskeletal movements, respectively. Simultaneous simulation of both domains is needed when interactions between tissue and movement are of interest, but this has remained largely impractical due to the high computational cost. Here we present a method for the concurrent simulation of tissue and movement, in which state of the art methods are used in each domain, and communication occurs via a surrogate modeling system based on locally weighted regression. The surrogate model only performs FE simulations when regression from previous results is not within a user-specified tolerance. For proof of concept and to illustrate feasibility, the methods were demonstrated on an optimization of jumping movement using a planar musculoskeletal model coupled to a FE model of the foot. To test the relative accuracy of the surrogate model outputs against those of the FE model, a single forward dynamics simulation was performed with FE calls at every integration step and compared with a corresponding simulation with the surrogate model included. Neural excitations obtained from the jump height optimization were used for this purpose and root mean square (RMS) difference between surrogate and FE model outputs (ankle force and moment, peak contact pressure and peak von Mises stress) were calculated. Optimization of the jump height required 1800 iterations of the movement simulation, each requiring thousands of time steps. The surrogate modeling system only used the FE model in 5% of time steps, i.e., a 95% reduction in computation time. Errors introduced by the surrogate model were less than 1 mm in jump height and RMS errors of less than 2 N in ground reaction force, 0.25 Nm in ankle moment, and 10 kPa in peak tissue stress. Adaptive surrogate modeling based on local regression allows efficient concurrent simulations of tissue mechanics and musculoskeletal movement.