> 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.
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.