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Modeling of Soft Object Deformation using Finite Element Differential Neural Networks

机译：基于有限元差分神经网络的软物体变形建模

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This paper presents a nonparametric modeling based on Finite Element Differential Neural Networks (FEDNN) of soft object deformation dynamics. The construction of the adaptive model is based on the Finite Element Method (FEM) and the Differential Neural Network (DNN) methodology. The input is taken by the displacement at each node collected from experimental data obtained from a motion capture system. A soft object sample is characterized using an equipment for stress tests and the nodes are collocated in its surface. The nodes information is used only to train the FEDNN. To verify the qualitative behavior of the suggested methodology, here the estimated trajectories are compared with the Motion Capture spatial position vector of the surface of the sample soft object. The adaptive laws for weights ensure the closeness of FEDNN trajectories to the tissue dynamics.
机译：本文提出了基于有限元差分神经网络（FEDNN）的软物体变形动力学的非参数建模。自适应模型的构建基于有限元方法（FEM）和微分神经网络（DNN）方法。输入是通过从运动捕获系统获得的实验数据收集的每个节点处的位移获取的。使用压力测试设备对软物体样本进行表征，并将节点放置在其表面中。节点信息仅用于训练FEDNN。为了验证所建​​议方法的定性行为，此处将估算的轨迹与样品软物体表面的运动捕捉空间位置矢量进行比较。权重的自适应定律可确保FEDNN轨迹与组织动力学的接近性。

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