DiffEqSensitivity.jl
StochasticDiffEq.jl
| DiffEqSensitivity.jl | StochasticDiffEq.jl | |
|---|---|---|
| 2 | 1 | |
| 184 | 234 | |
| - | 0.4% | |
| 9.5 | 7.8 | |
| almost 2 years 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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DiffEqSensitivity.jl
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[R] New directions in Neural Differential Equations
One reason is that it's not robust and has some odd counter example cases that can come up where the ODE solver is able to converge rapidly on the original problem but not so rapidly in the integral sense on the derivative values. One such case showed up in this issue, which was the impetus for the change in the forward-mode sense, while the reverse sense was changed in testing with direct quadratures (which will be mentioned in a bit).
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Odd Behavior: Neural network hybrid differential equation example
Thanks for letting us know. The fix is in https://github.com/SciML/DiffEqSensitivity.jl/pull/386 and hopefully that'll get released today.
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?
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.
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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
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.
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)
SciMLBook - Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Clapeyron.jl - Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC.