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.
Modulated adaptive fourier neural operators for temporal interpolation of weather forecasts.arXiv preprint arXiv:2410.18904
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NSIPF represents density via empirical particle measures and the field via a CNN trained on synthetic data, preserving mass and nonnegativity while simulating 3D multi-bump chemotaxis dynamics faster than finite difference or standard SIPF methods.
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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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An Efficient Particle-Field Algorithm with Neural Interpolation based on a Parabolic-Hyperbolic Chemotaxis System in 3D
NSIPF represents density via empirical particle measures and the field via a CNN trained on synthetic data, preserving mass and nonnegativity while simulating 3D multi-bump chemotaxis dynamics faster than finite difference or standard SIPF methods.