SciML / SciML/NeuralOperators.jl
Implement GNOT (General Neural 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 GNOT, a general-purpose transformer-based neural operator with heterogeneous cross-attention and linear complexity.
Reference
- Hao et al., "GNOT: A General Neural Operator Transformer for Operator Learning," ICML 2023. Paper
Description
GNOT uses heterogeneous normalized cross-attention to handle multiple types of input conditions (initial conditions, boundary conditions, forcing terms, PDE coefficients) within a single architecture. It achieves linear-time complexity through its attention mechanism, making it scalable to large grids. Designed as a general-purpose operator learning framework.
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
Start with the GNOT paper linked in the issue and inspect the NeuralOperators.jl repository to identify the relevant operator entry points. The issue names no files or tests, so the implementation scope and validation criteria need to be established before work begins; done should mean a working GNOT implementation matching the described heterogeneous attention and linear-complexity goals.
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
- 25/100