Clang.jl
DifferentialEquations.jl
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| Clang.jl | DifferentialEquations.jl | |
|---|---|---|
| 1 | 6 | |
| 210 | 2,622 | |
| 0.0% | 1.3% | |
| 0.0 | 0.0 | |
| 18 days ago | 3 days ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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.
Clang.jl
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A new C++ <-> Julia Wrapper: jluna
If you are interested in C++ interop you can also have a look at Clang.jl and CxxWrap.jl (the usual Julia package chaos applies, where the package mentioned in old talks and docs that you find on google is superseded by some others...)
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.
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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.
What are some alternatives?
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
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
ApproxFun.jl - Julia package for function approximation
Gridap.jl - Grid-based approximation of partial differential equations in Julia
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.
ReservoirComputing.jl - Reservoir computing utilities for scientific machine learning (SciML)
SciMLBenchmarks.jl - Benchmarks for scientific machine learning (SciML) software, scientific AI, and (differential) equation solvers
mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.
Tables.jl - An interface for tables in Julia
Molly.jl - Molecular simulation in Julia