DiffEqBase.jl
StochasticDiffEq.jl
DiffEqBase.jl | StochasticDiffEq.jl | |
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1 | 1 | |
297 | 234 | |
1.0% | 0.4% | |
9.3 | 7.8 | |
14 days ago | 3 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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DiffEqBase.jl
StochasticDiffEq.jl
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Writing unit tests in scientific computing
For stochastic processes you have to work a little bit more. However maybe the StochasticDiffEq.jl package can give some guiding there https://github.com/SciML/StochasticDiffEq.jl/tree/master/test
What are some alternatives?
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
SciMLSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
ComponentArrays.jl - Arrays with arbitrarily nested named components.
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]
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
18S096SciML - 18.S096 - Applications of Scientific Machine Learning