SciML / SciML/NeuralOperators.jl
Implement Multi-Fidelity DeepONet
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 41
- Forks
- 15
- Avg merge
- 13h 14m
- Merged PRs (30d)
- 12
Description
Summary
Implement Multi-Fidelity DeepONet for learning operators from datasets with mixed fidelity levels (e.g., coarse and fine simulations).
Reference
- Howard et al., "Multifidelity deep operator networks for data-driven and physics-informed problems," Journal of Computational Physics, 2023. DOI: 10.1016/j.jcp.2023.112462
Description
Multi-Fidelity DeepONet uses a composite architecture with separate DeepONet components for low-fidelity and high-fidelity mappings. The low-fidelity component learns from abundant cheap data, while the high-fidelity component learns the correction/residual from sparse expensive data. This is particularly useful in engineering applications where high-fidelity simulations are costly.
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 existing DeepONet implementation in NeuralOperators.jl and the cited Howard et al. paper to identify how low- and high-fidelity data should be represented. Define the architecture and validation criteria for learning a low-fidelity mapping and a high-fidelity correction, then add tests showing the combined model handles mixed-fidelity datasets.
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