FluidNet – Accelerating Eulerian Fluid Simulation with Convolutional Networks

Re:FluidNet – Accelerating Eulerian Fluid Simulation with Co... by phorese on Wednesday November 23, 2016 @02:14AM (#13020762)

> 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.

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Re:FluidNet – Accelerating Eulerian Fluid Simulation with Co... by londons_explore on Wednesday November 23, 2016 @05:58AM (#13021641)

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).

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