SciML / SciML/ModelOrderReduction.jl
Dominant Subspaces Projection Model Reduction
Nobody has claimed this yet.
- 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 dominant subspaces projection model reduction for LTI systems.
Reference list (pyMOR LTI MOR checklist): https://github.com/pymor/pymor/issues/388#issuecomment-892486556
https://doi.org/10.1016/j.laa.2006.01.007
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 review the pyMOR LTI MOR checklist in issue #388, followed by the linked LAA paper. Then locate the existing LTI model-reduction entry points in the repository. Done means dominant subspaces projection model reduction is implemented for LTI systems and aligned with the referenced checklist.
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
- 35/100