How Does Distribution Shift Shape Pretraining Gains in Neural PDE Surrogates?
Pretrained neural PDE surrogates reduce CFD data needs 3.25× when geometry changes; quantifies distribution shift effects on pretraining gains.
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Pretrained neural PDE surrogates reduce CFD data needs 3.25× when geometry changes; quantifies distribution shift effects on pretraining gains.
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Framework unifying physics-informed neural networks and finite element methods via Gauss-Newton discretization for PDE solving.
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HiLNO applies hierarchical latent representations with multi-scale supervision to improve neural operator learning for PDEs on general geometries.
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