Slow slip events (SSEs) have been observed in many subduction zones and are understood to result from frictional unstable slip on the plate interface. The diversity of their characteristics and the fact that interplate slip can also be seismic suggest that frictional properties are heterogeneous. We are however lacking methods to constrain spatial distribution of frictional properties. In this paper, we employ Physics-Informed Neural Networks (PINNs) to achieve this goal using a synthetic model inspired by the long-term SSEs observed in the Bungo channel. PINN is a deep learning technique which can be used to solve the physics-based differential equations and determine the model parameters from observations. To examine the potential of our proposed method, we execute a series of numerical experiments. We start with an idealized case where it is assumed that fault slip is directly observed. We next move to a more realistic case where the synthetic surface displacement velocity data are observed by virtual GNSS stations. The geometry and friction properties of the velocity weakening region, where the slip instability develops, are well estimated, especially if surface displacement velocities above the velocity weakening region are observed. Our PINN-based method can be seen as an inversion technique with the regularization constraint that fault slip obeys a particular friction law. This approach remediates the issue that standard regularization techniques are based on non-physical constraints. These results of numerical experiments reveal that the PINN-based method is a promising approach for estimating the spatial distribution of friction parameters from GNSS observation.