We describe a framework that can integrate prior physical information, e.g., the presence of kinematic constraints, to support data-driven simulation in multi-body dynamics. Unlike other approaches, e.g., Fully-connected Neural Network (FCNN) or Recurrent Neural Network (RNN)-based methods that are used to model the system states directly, the proposed approach embraces a Neural Ordinary Differential Equation (NODE) paradigm that models the derivatives of the system states. A central part of the proposed methodology is its capacity to learn the multibody system dynamics from prior physical knowledge and constraints combined with data inputs. This learning process is facilitated by a constrained optimization approach, which ensures that physical laws and system constraints are accounted for in the simulation process. The models, data, and code for this work are publicly available as open source at https://github.com/uwsbel/sbel-reproducibility/tree/master/2024/MNODE-code.

翻译：本文提出一种能够整合先验物理信息（例如运动学约束的存在）以支持多体动力学数据驱动仿真的框架。与直接建模系统状态的全连接神经网络或循环神经网络等方法不同，所提方法采用神经常微分方程范式对系统状态的导数进行建模。该方法的核心在于其能够结合先验物理知识、约束条件与数据输入来学习多体系统动力学。这一学习过程通过约束优化方法实现，确保仿真过程中兼顾物理定律与系统约束。本工作的模型、数据及代码已在https://github.com/uwsbel/sbel-reproducibility/tree/master/2024/MNODE-code开源发布。