SciML / SciML/ModelOrderReduction.jl
Empirical Cubature Method (ECM) Hyper-Reduction
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new-algorithm
- Dominant language
- Julia
- Stars
- 43
- Forks
- 8
- Avg merge
- 11h 47m
- Merged PRs (30d)
- 14
Description
From the catch-all tracking issue https://github.com/SciML/ModelOrderReduction.jl/issues/78
Implement the Empirical Cubature Method (ECM) project-then-approximate hyper-reduction via sparse mesh sampling / cubature weights.
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 linked ECM paper to understand the intended project-then-approximate hyper-reduction approach. The issue names no files or tests; completion would require implementing sparse mesh sampling and cubature weights for the Empirical Cubature Method.
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
- 25/100