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.
Seidman, Georgios Kissas, George J
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
NOFE is a neural operator method for continuous dimensionality reduction using Graph Kernel Operators that outperforms PCA, t-SNE and UMAP on local structure preservation and sampling independence in datasets including ERA5 climate reanalysis.
DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.
citing papers explorer
-
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.
-
NOFE - Neural Operator Function Embedding
NOFE is a neural operator method for continuous dimensionality reduction using Graph Kernel Operators that outperforms PCA, t-SNE and UMAP on local structure preservation and sampling independence in datasets including ERA5 climate reanalysis.
-
Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.