ReservoirComputing.jl
Catalyst.jl
| ReservoirComputing.jl | Catalyst.jl | |
|---|---|---|
| 1 | 2 | |
| 200 | 421 | |
| 1.0% | 1.2% | |
| 8.5 | 9.5 | |
| about 9 hours ago | 9 days ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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ReservoirComputing.jl
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Scientists develop the next generation of reservoir computing
Not just similar, the same. If you look through the documentation you'll see that https://github.com/SciML/ReservoirComputing.jl is a collection of reservoir architectures with high performance implementations, and some of our recent research has been pulling reservoir computing to the continuous domain for stiff ODEs (think of it almost like a neural ODE that you do not need to train via gradient descent): https://arxiv.org/abs/2010.04004 . We are definitely digging through this paper with some fascination and will incorporate a lot of its advancements into the software.
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?
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.
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
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
ParameterizedFunctions.jl - A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
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
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Causal.jl - Causal.jl - A modeling and simulation framework adopting causal modeling approach.
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
MomentClosure.jl - Tools to generate and study moment equations for any chemical reaction network using various moment closure approximations
Unityper.jl
casadi - CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.