SciMLTutorials.jl VS DiffEqOperators.jl

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

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SciMLTutorials.jl DiffEqOperators.jl
1 3
705 281
0.1% -
3.1 4.6
7 months ago 10 months ago
CSS Julia
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

SciMLTutorials.jl

Posts with mentions or reviews of SciMLTutorials.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-21.

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.

  • 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.

What are some alternatives?

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

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

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

SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]

ApproxFun.jl - Julia package for function approximation

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

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)

oxide-enzyme - Enzyme integration into Rust. Experimental, do not use.

18337 - 18.337 - Parallel Computing and Scientific Machine Learning

julia - The Julia Programming Language

auto-07p - AUTO is a publicly available software for continuation and bifurcation problems in ordinary differential equations originally written in 1980 and widely used in the dynamical systems community.

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.