CATO learns a continuous latent chart for efficient axial attention on PDE meshes and adds derivative-aware supervision to improve accuracy and reduce oversmoothing on general geometries.
Pde solvers should be local: Fast, stable rollouts with learned local stencils
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TF-SNO introduces state-adaptive time-frequency gating in spectral neural operators to model non-stationary PDEs, achieving lower errors than baselines on 1D and 2D benchmarks especially in long rollouts.
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CATO: Charted Attention for Neural PDE Operators
CATO learns a continuous latent chart for efficient axial attention on PDE meshes and adds derivative-aware supervision to improve accuracy and reduce oversmoothing on general geometries.
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TF-SNO: Time-Frequency Gated Spectral Neural Operators for Learning Non-Stationary Partial Differential Equations
TF-SNO introduces state-adaptive time-frequency gating in spectral neural operators to model non-stationary PDEs, achieving lower errors than baselines on 1D and 2D benchmarks especially in long rollouts.