  Solving partial differential equations (PDE) is an indispensable part of many
branches of science as many processes can be modelled in terms of PDEs.
However, recent numerical solvers require manual discretization of the
underlying equation as well as sophisticated, tailored code for distributed
computing. Scanning the parameters of the underlying model significantly
increases the runtime as the simulations have to be cold-started for each
parameter configuration. Machine Learning based surrogate models denote
promising ways for learning complex relationship among input, parameter and
solution. However, recent generative neural networks require lots of training
data, i.e. full simulation runs making them costly. In contrast, we examine the
applicability of continuous, mesh-free neural solvers for partial differential
equations, physics-informed neural networks (PINNs) solely requiring
initial/boundary values and validation points for training but no simulation
data. The induced curse of dimensionality is approached by learning a domain
decomposition that steers the number of neurons per unit volume and
significantly improves runtime. Distributed training on large-scale cluster
systems also promises great utilization of large quantities of GPUs which we
assess by a comprehensive evaluation study. Finally, we discuss the accuracy of
GatedPINN with respect to analytical solutions -- as well as state-of-the-art
numerical solvers, such as spectral solvers.
