Accurate simulation of deformable linear object (DLO) dynamics is challenging if the task at hand requires a human-interpretable model that also yields fast predictions. To arrive at such a model, we draw inspiration from the rigid finite element method (R-FEM) and model a DLO as a serial chain of rigid bodies whose internal state is unrolled through time by a dynamics network. As this state is not observed directly, the dynamics network is trained jointly with a physics-informed encoder which maps observed motion variables to the DLO's hidden state. To encourage that the state acquires a physically meaningful representation, we leverage the forward kinematics of the underlying R-FEM model as a decoder. Through robot experiments we demonstrate that the proposed architecture provides an easy-to-handle, yet capable DLO dynamics model yielding physically interpretable predictions from partial observations. The project code is available at: \url{https://tinyurl.com/fei-networks}

翻译：精确模拟可变形线性物体（DLO）的动力学是一个挑战，尤其是在需要获得既具有人类可解释性又能快速预测的模型时。为构建此类模型，我们从刚性有限元方法（R-FEM）中汲取灵感，将DLO建模为一系列刚性体的串行链，其内部状态通过动力学网络随时间展开。由于该状态无法直接观测，动力学网络与一个物理信息编码器联合训练，该编码器将观测到的运动变量映射到DLO的隐状态。为使状态获得具有物理意义的表示，我们利用底层R-FEM模型的正向运动学作为解码器。通过机器人实验证明，所提出的架构提供了一种易于处理且功能强大的DLO动力学模型，能够从部分观测中生成物理可解释的预测。项目代码见：\url{https://tinyurl.com/fei-networks}