  Neural Networks as fast physics simulators have a large potential for many
engineering design tasks. Prerequisites for a wide-spread application are an
easy-to-use workflow for generating training datasets in a reasonable time, and
the capability of the network to generalize to unseen systems. In contrast to
most previous works where training systems are similar to the evaluation
dataset, we propose to adapt the type of training system to the network
architecture. Specifically, we apply a fully convolutional network and, thus,
design 3D systems of randomly located voxels with randomly assigned physical
properties. The idea is tested for the transient heat diffusion in electronic
systems. Training only on random "Minecraft" systems, we obtain good
generalization to electronic systems four times as large as the training
systems (one-step prediction error of 0.07% vs 0.8%).
