auto-07p
DiffEqBase.jl
| auto-07p | DiffEqBase.jl | |
|---|---|---|
| 2 | 1 | |
| 113 | 297 | |
| 1.8% | 1.0% | |
| 7.8 | 9.3 | |
| 26 days ago | 5 days ago | |
| Fortran | Julia | |
| - | GNU General Public License v3.0 or later |
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auto-07p
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auto-07p VS BifurcationKit.jl - a user suggested alternative
2 projects | 11 Feb 2024
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Error: multiple definitions of block data
The odepack.o is from a .f file and homcont.o is from a .f90. I'm not sure how to fix this error. I can't edit the homcont.f90 file, but I can edit odepack.f. Can someone help me with this? Thanks :)
DiffEqBase.jl
What are some alternatives?
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)
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.
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
ComponentArrays.jl - Arrays with arbitrarily nested named components.
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
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
18S096SciML - 18.S096 - Applications of Scientific Machine Learning