The accurate simulation of deformable linear object (DLO) dynamics is challenging if the task at hand requires a human-interpretable and data-efficient model that also yields fast predictions. To arrive at such 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 mapping observed motion variables to the body chain's state. To encourage that the state acquires a physically meaningful representation, we leverage the forward kinematics (FK) of the underlying R-FEM model as a decoder. We demonstrate in a robot experiment that this architecture - being termed "Finite element inspired network" - forms 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：可变形线性物体动力学的精确模拟在需要人类可解释、数据高效且能快速预测的模型时极具挑战性。为构建此类模型，我们从刚性有限元方法中汲取灵感，将可变形线性物体建模为由动力学网络随时间展开内部状态的刚性体串联链。由于该状态无法直接观测，我们联合训练动力学网络与物理信息编码器，该编码器将观测到的运动变量映射到刚体链的状态。为促使状态获得具有物理意义的表示，我们利用底层刚性有限元模型的正向运动学作为解码器。我们在机器人实验中证明，这种被称为“有限元启发网络”的架构形成了一种易于处理且功能强大的可变形线性物体动力学模型，能够从部分观测中产生物理可解释的预测。项目代码发布于：\url{https://tinyurl.com/fei-networks}