DiffEqBase.jl VS 18S096SciML

Compare DiffEqBase.jl vs 18S096SciML and see what are their differences.

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DiffEqBase.jl 18S096SciML
1 1
297 303
1.0% 1.0%
9.3 2.7
16 days ago about 2 years ago
Julia HTML
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.

DiffEqBase.jl

Posts with mentions or reviews of DiffEqBase.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-29.

18S096SciML

Posts with mentions or reviews of 18S096SciML. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing DiffEqBase.jl and 18S096SciML you can also consider the following projects:

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.

SciMLBook - Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

cs229-2019-summer - All notes and materials for the CS229: Machine Learning course by Stanford University

ComponentArrays.jl - Arrays with arbitrarily nested named components.

DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)

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)

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

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

DiffEqFlux.jl - Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

StochasticDiffEq.jl - Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

JFVM.jl - A simple finite volume tool for Julia