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Application to 3D dataset

Open Berumott0 opened this issue 1 year ago • 2 comments

Hello, I am interested in applying the GNS approach to my dataset. Specifically, I intend to use ceramic fragments as nodes to predict the location of the fragments after the ceramic is impacted. However, after expanding my test dataset to 3D, I am having a bit of a problem with the dimensionality of the boundary distances, and the boundaries are never dimensionally aligned with the most_recent_position. Can you suggest how best to modify the current framework to accommodate the 3D dataset? Any guidance or advice would be appreciated.

Berumott0 avatar Dec 03 '24 14:12 Berumott0

We have data format documentation here. If the data is 3D, the "bounds" keyword in metadata should have 3 items where each item corresponds to the range of boundary for each dimension ((e.g., "bounds": [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9]])). Also, please check if the node feature length nnode_in aligns with your data.

yjchoi1 avatar Dec 03 '24 22:12 yjchoi1

We have data format documentation here. If the data is 3D, the "bounds" keyword in metadata should have 3 items where each item corresponds to the range of boundary for each dimension ((e.g., "bounds": [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9]])). Also, please check if the node feature length nnode_in aligns with your data.

I created a dataset myself, where the bounds are [[0, 10], [0, 10], [0, 40]]. However, during training, I encountered all loss values being NaN. Do these bounds and positions need to be normalized before use?

li-ruidong avatar Dec 10 '24 09:12 li-ruidong