SciML / SciML/NeuralOperators.jl
Implement Geo-FNO (Geometry-Aware Fourier Neural Operator)
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 41
- Forks
- 15
- Avg merge
- 13h 14m
- Merged PRs (30d)
- 12
Description
Summary
Implement Geo-FNO, which learns a deformation from arbitrary input geometries to a uniform latent grid where standard FFT can be applied.
Reference
- Li et al., "Fourier Neural Operator with Learned Deformations for PDEs on General Geometries," JMLR, 2023. arXiv:2207.05209
Description
Standard FNO requires uniform grids for FFT. Geo-FNO adds a learnable deformation network that maps arbitrary geometries (point clouds, meshes, irregular grids) to a uniform latent space, applies standard FNO layers, then maps back. This enables FNO to handle complex geometries while retaining FFT efficiency. The paper reports 10^5x speedups over numerical solvers.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No implementation files, tests, or entry point are named. Start with the referenced JMLR paper or arXiv article and locate the repository's existing FNO implementation; completion should cover deformation to a uniform latent grid, FNO processing, and mapping back for arbitrary geometries.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100