  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.
