NVIDIA/torch-harmonics
Differentiable signal processing on the sphere for PyTorch
Implements spherical harmonic transforms (SHT) and vector SHT using quadrature rules paired with FFTs for efficient projection onto associated Legendre polynomials and harmonic bases. Built entirely on PyTorch primitives for full differentiability, with support for distributed quadrature across multiple ranks and custom CUDA kernels for spherical convolutions. Enables applications like Spherical Fourier Neural Operators and differentiable PDE solvers on spherical domains.
650 stars. Actively maintained with 21 commits in the last 30 days.
Stars
650
Forks
65
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 12, 2026
Commits (30d)
21
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