  Modeling the dynamics of flexible objects has become an emerging topic in the
community as these objects become more present in many applications, e.g., soft
robotics. Due to the properties of flexible materials, the movements of soft
objects are often highly nonlinear and, thus, complex to predict. Data-driven
approaches seem promising for modeling those complex dynamics but often neglect
basic physical principles, which consequently makes them untrustworthy and
limits generalization. To address this problem, we propose a
physics-constrained learning method that combines powerful learning tools and
reliable physical models. Our method leverages the data collected from
observations by sending them into a Gaussian process that is physically
constrained by a distributed Port-Hamiltonian model. Based on the Bayesian
nature of the Gaussian process, we not only learn the dynamics of the system,
but also enable uncertainty quantification. Furthermore, the proposed approach
preserves the compositional nature of Port-Hamiltonian systems.
