The goal in our problem setting is, thus, to learn the objective function f T in the target domain D T by leveraging the knowledge from low-fidelity domain D S . The main focus is on devising a transfer learning approach for graph neural networks based on feature representation transfer. We propose extensions for two different learning settings: transductive and inductive learning. In the transductive transfer learning setup considered here, the target domain is constrained to the set of instances observed in the source dataset, i.e. X T ⊆ X S . Thus, the task in the target domain requires us to make predictions only at points observed in the source task/domain. In the inductive setting, we assume that source and target domains could differ in the marginal distribution of instances, i.e. ρ X S ̸ = ρ X T . For both learning settings, we assume that the source domain dataset is significantly larger as it is associated with low-fidelity simulations/approximations.