Melting Arctic sea ice fractures into floes, adversely affecting safe navigation. Short-term forecasts of sea ice motion are required, but remain challenging due to multi-scale spatiotemporal atmosphere-ice-ocean couplings. Limitations of current methods include: optical satellite data are high-resolution but may be blocked by clouds or unable to observe in polar night; passive microwave data are low-resolution; SAR data have high resolution, but exact overpasses can be days apart; and climate models have insufficient spatial resolution. Machine learning (ML) has the potential to solve the prediction problem but can require large amounts of training data with no guarantee that these models obey the laws of physics. Physics-informed neural networks (PINNs) reduce the amount of data required for learning by constraining the model to generate solutions that satisfy a given partial differential equation. PINNs are fully differentiable with respect to all input coordinates and free parameters. We present a PINN that characterizes sea ice motion and predicts sea ice deformation.