  Physics-Informed Neural Networks have shown unique utility in parameterising
the solution of a well-defined partial differential equation using automatic
differentiation and residual losses. Though they provide theoretical guarantees
of convergence, in practice the required training regimes tend to be exacting
and demanding. Through the course of this paper, we take a deep dive into
understanding the loss landscapes associated with a PINN and how that offers
some insight as to why PINNs are fundamentally hard to optimise for. We
demonstrate how PINNs can be forced to converge better towards the solution, by
way of feeding in sparse or coarse data as a regulator. The data regulates and
morphs the topology of the loss landscape associated with the PINN to make it
easily traversable for the minimiser. Data regulation of PINNs helps ease the
optimisation required for convergence by invoking a hybrid
unsupervised-supervised training approach, where the labelled data pushes the
network towards the vicinity of the solution, and the unlabelled regime
fine-tunes it to the solution.
