Ahorn
DifferentialEquations.jl
Ahorn | DifferentialEquations.jl | |
---|---|---|
4 | 6 | |
190 | 2,777 | |
0.0% | 1.5% | |
1.8 | 7.2 | |
over 2 years ago | 23 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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Ahorn
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It's probably not relatable for most of you since you all have amazing specs, but for me it is ;-;
For some reason I can easily do that and file explorer won't crash/take time to load. I even tried up to 400 tabs once and my computer worked just fine, maybe a little bit slower but nothing too noticeable. I think the problem is that the app that made me think of making this meme is Ahorn, and it's not optimized very well
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Compile a Julia program with GUI to an executable file
Completely unrelated project but these people have made a GUI application and distributed it across platforms written in Julia, may wanna check it out: https://github.com/CelestialCartographers/Ahorn
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uh oh, seeky
I just edited the map and added way too many seekers, map editor can be found here (will install a few things if you download it)
- Currently making a map, here it is! Suggestions are appreciated!
DifferentialEquations.jl
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Startups are building with the Julia Programming Language
This lists some of its unique abilities:
https://docs.sciml.ai/DiffEqDocs/stable/
The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me.
[1] https://lwn.net/Articles/834571/
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From Common Lisp to Julia
https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.
Also, if you take a look at a tutorial, say the tutorial video from 2018,
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When is julia getting proper precompilation?
It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.
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Julia 1.7 has been released
Let's even put raw numbers to it. DifferentialEquations.jl usage has seen compile times drop from 22 seconds to 3 seconds over the last few months.
https://github.com/SciML/DifferentialEquations.jl/issues/786
- Suggest me a Good library for scientific computing in Julia with good support for multi-core CPUs and GPUs.
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DifferentialEquations compilation issue in Julia 1.6
https://github.com/SciML/DifferentialEquations.jl/issues/737 double posted, with the answer here. Please don't do that.
What are some alternatives?
julia - The Julia Programming Language
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Celeste-ARM64 - Utilities for getting Celeste to work on the Nintendo Switch (and ARM64 Linux in general)
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
DataFrames.jl - In-memory tabular data in Julia
Gridap.jl - Grid-based approximation of partial differential equations in Julia
ApproxFun.jl - Julia package for function approximation
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms
CUDA.jl - CUDA programming in Julia.
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
ReservoirComputing.jl - Reservoir computing utilities for scientific machine learning (SciML)