Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
Scalable and equivariant spherical cnns by discrete-continuous (disco) convolutions.arXiv preprint arXiv:2209.13603
5 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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representative citing papers
LSR-Net learns long-short-range operators for pattern dynamics on manifolds via Fourier multipliers and Gaussian gridding, showing lower RMSE than SFNO on Allen-Cahn and similar systems.
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
GSNO uses position-dependent spherical Green's functions to create flexible neural operators that adapt to non-equivariant systems on spheres while keeping spectral efficiency and grid invariance.
A survey diagnosing panoramic scene understanding as a field that converged on compatibility-preserving geometric adaptation rather than sphere-native modeling, while its evaluation protocols systematically fail to measure spherical understanding.
citing papers explorer
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Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
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LSR-Net: Long-Short-Range Operator Learning for Pattern Dynamics on Manifolds
LSR-Net learns long-short-range operators for pattern dynamics on manifolds via Fourier multipliers and Gaussian gridding, showing lower RMSE than SFNO on Allen-Cahn and similar systems.
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Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
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Generalized Spherical Neural Operators: Green's Function Formulation
GSNO uses position-dependent spherical Green's functions to create flexible neural operators that adapt to non-equivariant systems on spheres while keeping spectral efficiency and grid invariance.
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Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling
A survey diagnosing panoramic scene understanding as a field that converged on compatibility-preserving geometric adaptation rather than sphere-native modeling, while its evaluation protocols systematically fail to measure spherical understanding.