RigidBodySim.jl
ComponentArrays.jl
Our great sponsors
RigidBodySim.jl | ComponentArrays.jl | |
---|---|---|
0 | 1 | |
68 | 270 | |
- | - | |
0.0 | 7.4 | |
almost 4 years ago | 13 days ago | |
Jupyter Notebook | Julia | |
GNU General Public License v3.0 or later | 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.
RigidBodySim.jl
We haven't tracked posts mentioning RigidBodySim.jl yet.
Tracking mentions began in Dec 2020.
ComponentArrays.jl
-
Recursion absolutely necessary for distributed computing?
But for these to be as fast as say an Array when being used as the object in a differential equation solve or as the underlying construct of a nonlinear optimization, you would need the compiler to elide the struct construction which it doesn't always do. This is why the tools evolved to be around things like https://github.com/jonniedie/ComponentArrays.jl instead, where it's an Array-backed object with a higher level. Such immutable objects are used in these array-like contexts when the problems are small enough (FieldVectors or SLVector LabelledArrays.jl in DiffEq), and such applications work well in Haskell as well, but I haven't seen a compiler do well with say a 1,000 ODE model written in this style. And it's not quite an extreme case if it's what people are doing daily.
What are some alternatives?
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
RayTracer.jl - Differentiable RayTracing in Julia
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
FunctionalCollections.jl - Functional and persistent data structures for Julia
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
ControlSystems.jl - A Control Systems Toolbox for Julia
DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
transient_rotordynamic - transient dynamics of elastic rotors in journal bearings with Julia and Python
julia - The Julia Programming Language