oorb
Oceananigans.jl
| oorb | Oceananigans.jl | |
|---|---|---|
| 3 | 4 | |
| 54 | 878 | |
| - | 1.0% | |
| 5.3 | 9.5 | |
| about 2 months ago | 7 days ago | |
| Fortran | Julia | |
| GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
oorb
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Show HN: OpenOrb, a curated search engine for Atom and RSS feeds
Imagine my surprise to see OpenOrb, the standard open source software package for orbit determination and minor planet propagation, on the front page of HackerNews. Its interesting software with a beautiful theoretical basis in Bayesian statistics, and a gnarly Fortran codebase - I can’t wait to see the discussion!
Oh.
It’s one thing to land near a name in use. It is quite another to take it directly!
https://github.com/oorb/oorb
- Julia 1.10 Released
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NASA’s Double Asteroid Redirection Test Is a Smashing Success
Mostly Python and Fortran. See for example https://github.com/oorb/oorb.
The hardest problems are always the social ones. How do you get uptake of a new method, how do you get funding, how do you politely tell a collaboration they are doing the wrong thing, etc.
But if you mean pure technical stuff - the hardest problem I had to solve was rethinking some of the inner loops of the THOR algorithm. The problem was essentially to speed up a Hough transform in 6D space. Lots of time spent profiling CPU cache timings to get that fast.
Oceananigans.jl
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Julia 1.10 Released
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
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GPU vendor-agnostic fluid dynamics solver in Julia
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.
- Supercharged high-resolution ocean simulation with Jax
What are some alternatives?
thor - Tracklet-less Heliocentric Orbit Recovery
MATDaemon.jl
Torch.jl - Sensible extensions for exposing torch in Julia.
FiniteDiff.jl - Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support
threads - Threads for Lua and LuaJIT. Transparent exchange of data between threads is allowed thanks to torch serialization.
MITgcm - M.I.T General Circulation Model master code and documentation repository
Tidier.jl - Meta-package for data analysis in Julia, modeled after the R tidyverse.
Metal.jl - Metal programming in Julia
Lux.jl - Explicitly Parameterized Neural Networks in Julia
opendylan - Open Dylan compiler and IDE
Transformers.jl - Julia Implementation of Transformer models
julia-ml-from-scratch - Machine learning from scratch in Julia