SciML / SciML/NeuralOperators.jl
Implement Mamba Neural Operator (Alias-Free MNO)
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- Dominant language
- Julia
- Stars
- 41
- Forks
- 15
- Avg merge
- 13h 14m
- Merged PRs (30d)
- 12
Description
Summary
Implement the Mamba Neural Operator, which applies selective state-space models (Mamba) to PDE solving with global receptive fields and linear complexity.
Reference
- "Alias-Free Mamba Neural Operator," NeurIPS 2024. Paper
Description
The Mamba Neural Operator applies the Mamba selective state-space model architecture to operator learning. It achieves global receptive fields with linear complexity (vs quadratic for transformers), uses adaptive state-space matrices, and includes an alias-free design to prevent spectral aliasing. Reports up to ~90% error reduction over transformer baselines and greatly improved long-time stability for autoregressive rollouts.
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 the linked NeurIPS 2024 paper and inspecting the repository's existing neural-operator implementations to determine the integration point. Done means the alias-free Mamba Neural Operator is implemented with selective state-space modeling, global receptive fields, linear complexity, and support for PDE solving.
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