SciML / SciML/DataDrivenDiffEq.jl
Benchmark of examples in data driven discovery of forward operators
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 430
- Forks
- 58
- Avg merge
- 6h 29m
- Merged PRs (30d)
- 28
Description
DataDrivenDiffEq should demonstrate the SciML ecosystem tools and solutions to the same examples that are proposed in deepxde https://github.com/lululxvi/deepxde/tree/master/examples in Python.
deepxde has recently reciprocated with the Lotka-Volterra inverse example and it would be a great way to showcase and benchmark SciML tools: ModelingToolkit, DiffEqFlux, NeuralPDE, UDE, AD etc. against the Python solution for all the other examples. Do we already have the Lorenz inverse example somewhere in the works?
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 reviewing the linked DeepXDE Python examples and checking whether a Lorenz inverse example already exists or is in progress. Done means the requested SciML examples are demonstrated and benchmarked against the corresponding Python solutions, including the named Lotka-Volterra inverse example and relevant SciML tools.
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
- Mostly clear
- Newbie friendliness
- 25/100