SciML / SciML/NeuralOperators.jl
Implement HyperDeepONet
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 41
- Forks
- 15
- Avg merge
- 13h 14m
- Merged PRs (30d)
- 12
Description
Summary
Implement HyperDeepONet, which uses a hypernetwork to generate the weights of the trunk network.
Reference
- Lee et al., "HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork," 2024. arXiv:2312.15949
Description
HyperDeepONet replaces the fixed trunk network with a hypernetwork-generated trunk, where the hypernetwork takes the input function encoding and produces trunk weights. This enables learning of more complex operators with fewer parameters by making the trunk adaptive to each input function.
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
The issue names no files, tests, or entry points. Start by reviewing the repository's existing DeepONet implementation and the cited HyperDeepONet paper to determine the required architecture and integration points. Done means HyperDeepONet is implemented and verified with appropriate tests, though the issue does not specify those tests.
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