SciML / SciML/NeuralOperators.jl
Implement OFormer (Operator Transformer)
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 41
- Forks
- 15
- Avg merge
- 13h 14m
- Merged PRs (30d)
- 12
Description
Summary
Implement OFormer, an attention-based operator learning framework using self-attention and cross-attention with minimal assumptions on input/output sampling.
Reference
- Li et al., "Transformer for Partial Differential Equations' Operator Learning," 2022. arXiv:2205.13671
Description
OFormer uses self-attention to encode the input function, cross-attention to query at arbitrary output locations, and point-wise MLPs for nonlinear feature extraction. It makes few assumptions on the sampling pattern or query locations, providing flexibility for irregularly sampled data.
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 files, tests, or entry points are named. Start by reading Li et al.'s OFormer paper and then inspect the repository's existing operator-learning APIs. Done should include an OFormer implementation with self-attention, cross-attention, point-wise MLPs, and support for irregular input sampling and arbitrary query locations.
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