NOFE learns continuous function-to-function embeddings via graph kernel operators, outperforming PCA, t-SNE, and UMAP in local structure preservation on function-valued datasets like ERA5 while remaining robust to sampling changes.
Seidman, Georgios Kissas, George J
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SPAMoE reduces average MAE by 44.4% on OpenFWI datasets for full-waveform inversion via a spectral-preserving DINO encoder and dynamic frequency-band routing to specialized neural operators.
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NOFE -- Neural Operator Function Embedding
NOFE learns continuous function-to-function embeddings via graph kernel operators, outperforming PCA, t-SNE, and UMAP in local structure preservation on function-valued datasets like ERA5 while remaining robust to sampling changes.
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SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion
SPAMoE reduces average MAE by 44.4% on OpenFWI datasets for full-waveform inversion via a spectral-preserving DINO encoder and dynamic frequency-band routing to specialized neural operators.