SciMLTutorials.jl VS auto-07p

Compare SciMLTutorials.jl vs auto-07p and see what are their differences.

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. (by auto-07p)
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SciMLTutorials.jl auto-07p
1 2
708 113
0.4% 0.9%
3.1 6.7
8 months ago 16 days ago
CSS Fortran
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.

auto-07p

Posts with mentions or reviews of auto-07p. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-11.

What are some alternatives?

When comparing SciMLTutorials.jl and auto-07p you can also consider the following projects:

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

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)

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]

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

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

ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond

18337 - 18.337 - Parallel Computing and Scientific Machine Learning

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

DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

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

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