SciML / SciML/DataDrivenDiffEq.jl
Deep Symbolic Regression
Open
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 430
- Forks
- 58
- Avg merge
- 6h 29m
- Merged PRs (30d)
- 28
Description
It's essentially using linear sparse symbolic regression techniques as a fitness function for genetic algorithms to construct better bases.
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 by reading the linked paper and inspecting the repository's existing symbolic-regression and basis-construction entry points. The issue names no files or tests and does not define acceptance criteria; the work would first need a scoped design for the genetic-algorithm approach and a clear validation plan.
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