SciML / SciML/ModelOrderReduction.jl
DAE2FSM
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 DAE2FSM: learn a finite-state machine (Mealy) abstraction from continuous DAE/ODE input-output trajectories.
https://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-217.pdf
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 read the linked Berkeley report to understand the requested DAE2FSM approach. Identify the repository entry point for model-order-reduction features before determining the implementation scope; done means providing a Mealy finite-state-machine abstraction learned from continuous DAE/ODE input-output trajectories.
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
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100