SciML / SciML/DiffEqProblemLibrary.jl
Set of Problems for Scientific Machine Learning
Open
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 118
- Forks
- 40
- Avg merge
- 5h 16m
- Merged PRs (30d)
- 5
Description
https://ftp.mcs.anl.gov/pub/tech_reports/reports/P153.pdf provides a decent list of "real-world" applications that could serve as a good test-bed for
- https://github.com/SciML/ModelOrderReduction.jl @bowenszhu
- https://github.com/SciML/MethodOfLines.jl @xtalax
- https://github.com/SciML/NeuralOperators.jl @yuehhua
- ...
Ties together well with https://github.com/SciML/NeuralOperators.jl/issues/71
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
Read the linked ANL technical report first, then review the referenced ModelOrderReduction.jl, MethodOfLines.jl, and NeuralOperators.jl projects and NeuralOperators.jl issue 71. Clarify which real-world applications belong in DiffEqProblemLibrary.jl, how they should be represented, and which projects will coordinate the work; done means an agreed, actionable set of test problems.
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