Oceananigans.jl

I think it’s also the design philosophy. JuMP and ForwardDiff are great success stories and are packages very light on dependencies. I like those. The DiffEq library seems to pull you towards the SciML ecosystem and that might not be agreeable to everyone. For instance a known Julia project that simulates diff equations seems to have implemented their own solver https://github.com/CliMA/Oceananigans.jl

I‘m currently playing around with Oceananigans.jl (https://github.com/CliMA/Oceananigans.jl). Do you know how both are similar or different? Oceananigans.jl has really intuitive step-by-step examples and a great discussion page on GitHub.

> https://github.com/CliMA/Oceananigans.jl Off-topic, but I have to say that is one of my favourite package names in Julia. (A more recent one is [MATDaemon](https://github.com/jondeuce/MATDaemon.jl))

like some other commenters here, https://github.com/CliMA/Oceananigans.jl immediately comes to mind, maybe it would be fun to compare projects on this scale between JAX/Julia. > JAX offers more than just a JIT compiler: JAX functions are also differentiable if the downstream library is completely implemented in JAX (numba) ecosystem. Similar for Julia, except implementing fast code in Julia is natural, doesn't involve debugging 3 compilers (Cpython, Numba, Jax). Many python library is only differentiable because the 100x more effort were put in writing C/C++ backend, binding to python, and writing chain rules for foreign functions. I would imagine Julia to be a good fit for this direction in the future!