Pre-training a scalable graph U-net on 20,000 simulated CAD deformations lets it match or beat a from-scratch model on small benchmark datasets, with the paper reporting up to an 11.05% lower position RMSE when fine-tuned on 1/16 of the training data.
15 C E XPERIMENT DETAILS C.1 M ODEL CONFIGURATION MGN: For the Deforming Plate and Deformable Plate datasets, we adhere to the settings outlined in the original paper Pfaff et al
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Transfer learning in Scalable Graph Neural Network for Improved Physical Simulation
Pre-training a scalable graph U-net on 20,000 simulated CAD deformations lets it match or beat a from-scratch model on small benchmark datasets, with the paper reporting up to an 11.05% lower position RMSE when fine-tuned on 1/16 of the training data.