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I didn't find a link to the package in the article. It seems to be a quite capable package, and the given two examples that are trivial to solve analytically don't really do it justice.

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It’s probably the best library for DEs in any language. I’m pretty sure that DifferentialEquations.jl, by itself, is the reason many scientists and engineers are using Julia.

https://docs.sciml.ai/DiffEqDocs/stable/

I use Diffrax and my own stuff with Numba/CuPy JIT-ing. Can you compare the Python ecosystem for DEs with Julia according to your experience?

No, I’m not familiar with the Python ecosystem for DEs.

I didn't try it yet, but the docs looks great.

I have added a link to the package now. The first mention of the package in the body now contains a link to the official page for the package.

> and the given two examples that are trivial to solve analytically don't really do it justice.

Yes, I know. I plan to write more in the future.

Cool, the link is helpful, and the package is pretty cool. I'd just add that the examples are simple and be done with it, you can revisit with more later. Keep writing :)

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