DiffEqBase.jl
ComponentArrays.jl
| DiffEqBase.jl | ComponentArrays.jl | |
|---|---|---|
| 1 | 1 | |
| 297 | 276 | |
| 3.7% | - | |
| 9.3 | 7.0 | |
| 13 days ago | 7 days ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | MIT License |
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DiffEqBase.jl
ComponentArrays.jl
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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?
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.
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
RayTracer.jl - Differentiable RayTracing in Julia
OrdinaryDiffEq.jl - High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
ControlSystems.jl - A Control Systems Toolbox for Julia
18S096SciML - 18.S096 - Applications of Scientific Machine Learning
DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
StochasticDiffEq.jl - Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written in Julia