SciML / SciML/DataDrivenDiffEq.jl
Check domains
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 430
- Forks
- 58
- Avg merge
- 6h 29m
- Merged PRs (30d)
- 28
Description
Moving on to general symbolic regression, possible domain checks should be done on the problems. Checking for positive / negative definiteness, zero crossings etc. either should limit the use of functions or handle them in a safe way ( e.g. instead of 1/0 -> 1/eps() ).
This could be also used to infer functions itself, e.g. x^2 -> positive semidefinite, x^3 -> indefinite. So we might get hints ( similar to Feynman AI ) here.
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 files, tests, or entry points are named. Start by locating the general symbolic-regression workflow and determine where domain checks could be integrated; review how positive or negative definiteness, zero crossings, and unsafe function domains should affect function selection or evaluation. Done should include defined domain-check behavior and any resulting function-inference hints.
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
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100