MITgcm
Oceananigans.jl
| MITgcm | Oceananigans.jl | |
|---|---|---|
| 1 | 4 | |
| 357 | 1,109 | |
| 2.5% | 5.6% | |
| 9.0 | 9.8 | |
| 7 days ago | about 19 hours ago | |
| Fortran | Julia | |
| MIT License | 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.
MITgcm
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Greenland's glaciers are melting 100 times faster than estimated
Observational data used in this study is available at Sutherland et al. (2019b) for the LeConte Bay, Straneo (2022) for Helheim glacier/Sermilik Fjord, and Rignot and Schulz (2022) for Store fjord. The plume model used in this study builds on code developed by Cowton et al. (2015) and is distributed from Tom Cowton through his publicly available github open-source site https://github.com/tcowton/iceplume and open-source MITgcm checkpoint 65m https://github.com/MITgcm/MITgcm/archive/checkpoint65m.zip. Our modifications made to the iceplume package are available at https://github.com/KikiSchulz/iceplume_mod.
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?
E3SM - Energy Exascale Earth System Model source code. NOTE: use "maint" branches for your work. Head of master is not validated.
pyhpc-benchmarks - A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:
JuliaComputation - Repository for Common Ground C25
julia-ml-from-scratch - Machine learning from scratch in Julia
lightcurve-of-the-day - Animated transit lightcurve posted once a day to twitter
FiniteDiff.jl - Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support