The manipulation of deformable linear objects (DLOs) via model-based control requires an accurate and computationally efficient dynamics model. Yet, data-driven DLO dynamics models require large training data sets while their predictions often do not generalize, whereas physics-based models rely on good approximations of physical phenomena and often lack accuracy. To address these challenges, we propose a physics-informed neural ODE capable of predicting agile movements with significantly less data and hyper-parameter tuning. In particular, we model DLOs as serial chains of rigid bodies interconnected by passive elastic joints in which interaction forces are predicted by neural networks. The proposed model accurately predicts the motion of an robotically-actuated aluminium rod and an elastic foam cylinder after being trained on only thirty seconds of data. The project code and data are available at: \url{https://tinyurl.com/neuralprba}

翻译：基于模型的可变形线性物体操控需要精确且计算高效的动力学模型。然而，数据驱动的DLO动力学模型需要大量训练数据且其预测往往缺乏泛化能力，而基于物理的模型依赖于物理现象的近似假设且常精度不足。为解决这些挑战，我们提出了一种物理信息神经常微分方程模型，能够以显著更少的数据和超参数调优预测敏捷运动。具体而言，我们将DLO建模为由被动弹性关节连接的刚体串联链，其中相互作用力由神经网络预测。所提模型在仅用三十秒数据训练后，即可准确预测机器人驱动的铝杆和弹性泡沫圆柱体的运动。项目代码与数据公开于：\url{https://tinyurl.com/neuralprba}