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> 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"
by phorese 10y ago
> 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 10y agoEven 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).