WaterLily.jl
Oceananigans.jl
WaterLily.jl | Oceananigans.jl | |
---|---|---|
1 | 4 | |
550 | 914 | |
- | 4.3% | |
9.2 | 9.5 | |
5 days ago | 3 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | MIT License |
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WaterLily.jl
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GPU vendor-agnostic fluid dynamics solver in Julia
Their Github page has it: https://github.com/weymouth/WaterLily.jl
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?
oneAPI.jl - Julia support for the oneAPI programming toolkit.
MATDaemon.jl
FiniteDiff.jl - Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support
MITgcm - M.I.T General Circulation Model master code and documentation repository
Metal.jl - Metal programming in Julia
opendylan - Open Dylan compiler and IDE
julia-ml-from-scratch - Machine learning from scratch in Julia
XLA.jl - "Maybe we have our own magic."
ClimateMachine.jl - Climate Machine: an Earth System Model that automatically learns from data
dio - Decentralized Impact Organizations for the Climate
pyhpc-benchmarks - A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:
Lux.jl - Explicitly Parameterized Neural Networks in Julia