[phorese]

> As far as I can tell, the network is trained on a specific geometry and must be re-trained if the geometry changes.

The model is trained on a "training set" of various 3D models, and tested on a "test set" of different 3D models. The network can generalize to other 3D models and does not need retraining (see e.g. last part of abstract).

I think what they mean is that they would need to retrain the model if the boundary conditions at the outer boundary of the "studio" (the simulation domain) changed.

[londons_explore]

Even this isn't true. The model is fully convolutional, so is independent of location within the studio (as physics should be).

I expect it would need retraining for different material properties (eg. different densities).