JuMP.jl
Catalyst.jl
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| JuMP.jl | Catalyst.jl | |
|---|---|---|
| 3 | 2 | |
| 2,134 | 421 | |
| 1.5% | 3.6% | |
| 9.3 | 9.5 | |
| about 16 hours ago | 6 days ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | 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.
JuMP.jl
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Optimization
JuMP.jl is my personal go-to when solving "big" optimization problems in Julia (maybe it's overkill for your application).
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Multiple dispatch: Common Lisp vs Julia
A 100+ contributor project
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Julia macros
Macros are very useful if you want to create Domain Specific Languages (DSLs), see https://github.com/jump-dev/JuMP.jl or if you want to transpile a subset of Julia to another language or say GPU code.
Catalyst.jl
- Julia macros
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Should I switch over completely to Julia from Python for numerical analysis/computing?
ModelingToolkit.jl adds a different spin on this by noting what makes a good modeling system isn't top down but a system that allows for bottom up contributions. ModelingToolkit is built on Symbolics.jl which uses OSCAR.jl etc., so every time the symbolics community gets better ModelingToolkit.jl gets better. It connects to the whole SciML ecosystem, so any improvement to any of the SciML interface packages is directly an improvement to ModelingToolkit.jl. ModelingToolkit is made to be a set of composable compiler abstractions called transformations, so anyone can add new packages that do new transformations that improve the ecosystem. One that I really like is MomentClosure.jl which symbolically transforms stochastic ModelingToolkit models (ReactionSystem) to approximate symbolic ODESystem models of the moments. And there's domain-specific langauges like Catalyst.jl being built on the interface to give more ways to build models, which is spawning the biocommunity to make model importers into the symbolic forms, when then feeds more ODE models into the same compiler. JuliaSim is then building on this ecosystem, adding cloud infrastructure that is special-purpose made for doing parallel computations of these models, automatic symbolic model discovery from data, automatic generation of approximate models with machine learning, and tying the Julia Computing compiler team into the web that is building this ecosystem.
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
ComponentArrays.jl - Arrays with arbitrarily nested named components.
ParameterizedFunctions.jl - A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
NumericalAlgorithms.jl - [DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
MuladdMacro.jl - This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem
OMLT - Represent trained machine learning models as Pyomo optimization formulations
Causal.jl - Causal.jl - A modeling and simulation framework adopting causal modeling approach.
MomentClosure.jl - Tools to generate and study moment equations for any chemical reaction network using various moment closure approximations
ConstructiveGeometry.jl - Algorithms and syntax for building CSG objects within Julia.
ReservoirComputing.jl - Reservoir computing utilities for scientific machine learning (SciML)