SciML / SciML/ModelOrderReduction.jl
Singular Perturbation Approximation
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 singular perturbation approximation / residualization for LTI model reduction (retain slow dynamics by setting fast-state derivatives to zero).
https://people.kth.se/~hsan/modred_files/intro_modred.pdf
Reference list (pyMOR LTI MOR checklist): https://github.com/pymor/pymor/issues/388#issuecomment-892486556
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
No implementation file, test, or entry point is named. Start with tracking issue #78, then read the linked introduction and pyMOR LTI MOR checklist to determine the expected singular perturbation or residualization interface. Done means LTI model reduction retains the slow dynamics by setting fast-state derivatives to zero.
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
- 38/100