auto-07p VS SciMLTutorials.jl

Compare auto-07p vs SciMLTutorials.jl 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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auto-07p SciMLTutorials.jl
2 1
113 709
1.8% 0.6%
7.8 1.5
26 days ago 9 days ago
Fortran CSS
- 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.

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.

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.

What are some alternatives?

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

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

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

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]

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

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

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

18337 - 18.337 - Parallel Computing and Scientific Machine Learning

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

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

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