SciML / SciML/DataDrivenDiffEq.jl
Common methods for numerically approximating derivatives
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- Julia
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Description
The function "similar" in basis.jl appears to apply the finite difference method which is appropriate for smooth data.
Perhaps there should be an option in "similar" as well as in ModelingToolkit for non-smooth and noisy data like Total variation regularized derivative (see R. Chartrand, Numerical Differentiation of Noisy, Nonsmooth Data, ISRN Applied Mathematics, 2011).
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 similar function in basis.jl and trace how derivative approximation is configured in ModelingToolkit. Review the cited Chartrand paper and existing finite-difference behavior, then define the supported option and verify that nonsmooth and noisy data use the intended regularized derivative method.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100