  In this work, we present a deep collocation method for three dimensional
potential problems in nonhomogeneous media. This approach utilizes a physics
informed neural network with material transfer learning reducing the solution
of the nonhomogeneous partial differential equations to an optimization
problem. We tested different cofigurations of the physics informed neural
network including smooth activation functions, sampling methods for collocation
points generation and combined optimizers. A material transfer learning
technique is utilised for nonhomogeneous media with different material
gradations and parameters, which enhance the generality and robustness of the
proposed method. In order to identify the most influential parameters of the
network configuration, we carried out a global sensitivity analysis. Finally,
we provide a convergence proof of our DCM. The approach is validated through
several benchmark problems, also testing different material variations.
