DiffEqOperators.jl VS ReservoirComputing.jl

Compare DiffEqOperators.jl vs ReservoirComputing.jl and see what are their differences.

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DiffEqOperators.jl ReservoirComputing.jl
3 1
281 211
- 0.9%
4.6 8.9
over 1 year ago 9 days ago
Julia Julia
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.

DiffEqOperators.jl

Posts with mentions or reviews of DiffEqOperators.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-30.
  • Julia 1.7 has been released
    15 projects | news.ycombinator.com | 30 Nov 2021
    >I hope those benchmarks are coming in hot

    M1 is extremely good for PDEs because of its large cache lines.

    https://github.com/SciML/DiffEqOperators.jl/issues/407#issue...

    The JuliaSIMD tools which are internally used for BLAS instead of OpenBLAS and MKL (because they tend to outperform standard BLAS's for the operations we use https://github.com/YingboMa/RecursiveFactorization.jl/pull/2...) also generate good code for M1, so that was giving us some powerful use cases right off the bat even before the heroics allowed C/Fortran compilers to fully work on M1.

  • Why are NonlinearSolve.jl and DiffEqOperators.jl incompatible with the latest versions of ModelingToolkit and Symbolics!!!? Symbolics and ModelingToolkit are heavily downgraded when those packages are added.
    1 project | /r/Julia | 20 Aug 2021
    (b) DiffEqOperators.jl is being worked on https://github.com/SciML/DiffEqOperators.jl/pull/467 .
  • What's Bad about Julia?
    6 projects | news.ycombinator.com | 26 Jul 2021
    I like that they are colored now, but really what needs to be added is type parameter collapasing. In most cases, you want to see `::Dual{...}`, i.e. "it's a dual number", not `::Dual{typeof(ODESolution{sfjeoisjfsfsjslikj},sfsef,sefs}` (these can literally get to 3000 characters long). As an example of this, see the stacktraces in something like https://github.com/SciML/DiffEqOperators.jl/issues/419 . The thing is that it gives back more type information than the strictest dispatch: no function is dispatching off of that first 3000 character type parameter, so you know that printing that chunk of information is actually not informative to any method decisions. Automated type abbreviations could take that heuristic and chop out a lot of the cruft.

ReservoirComputing.jl

Posts with mentions or reviews of ReservoirComputing.jl. We have used some of these posts to build our list of alternatives and similar projects.
  • Scientists develop the next generation of reservoir computing
    1 project | news.ycombinator.com | 22 Sep 2021
    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.

What are some alternatives?

When comparing DiffEqOperators.jl and ReservoirComputing.jl you can also consider the following projects:

Gridap.jl - Grid-based approximation of partial differential equations 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.

FourierFlows.jl - Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains

NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

BoundaryValueDiffEq.jl - Boundary value problem (BVP) solvers for scientific machine learning (SciML)

Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

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

MethodOfLines.jl - Automatic Finite Difference PDE solving with Julia SciML

ApproxFun.jl - Julia package for function approximation

julia - The Julia Programming Language

Infiltrator.jl - No-overhead breakpoints in Julia

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