SciML / SciML/ModelOrderReduction.jl
GNAT Hyper-Reduction
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- Dominant language
- Julia
- Stars
- 43
- Forks
- 8
- Avg merge
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- Merged PRs (30d)
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Description
From the catch-all tracking issue https://github.com/SciML/ModelOrderReduction.jl/issues/78
Implement the Gauss-Newton Approximation Tensor (GNAT) approximate-then-project hyper-reduction method for nonlinear projection-based ROMs (DEIM is already in-tree; see closed #12).
https://link.springer.com/article/10.1007/s11831-025-10299-4
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 catch-all tracking issue #78 and the existing DEIM implementation referenced in this issue, then read the linked GNAT paper. Define the integration point for nonlinear projection-based ROMs and compare the result with DEIM. Done means GNAT approximate-then-project hyper-reduction is implemented and covered by suitable validation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- hpc
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100