SciML / SciML/NeuralOperators.jl
Implement Latent Spectral Model (LSM)
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- Dominant language
- Julia
- Stars
- 41
- Forks
- 15
- Avg merge
- 13h 14m
- Merged PRs (30d)
- 12
Description
Summary
Implement LSM, which encodes input functions into a latent space, constructs an orthogonal spectral basis there, and composes mappings using multiple basis operators.
Reference
- Wu et al., "Solving High-Dimensional PDEs with Latent Spectral Models," ICML 2023. Paper
Description
LSM uses cross-attention to encode input functions into a latent space, then constructs orthogonal basis functions in that latent space (inspired by classical spectral methods). The operator mapping is decomposed into multiple basis operators, enabling efficient learning for high-dimensional PDEs.
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 by reading the linked ICML 2023 paper and the issue description to understand the LSM architecture: cross-attention encoding, latent orthogonal basis construction, and multiple basis operators. Done means implementing the described LSM in NeuralOperators.jl and verifying that its latent spectral mappings work for high-dimensional PDE operator learning.
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