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.
SciMLSensitivity.jl is a component package in the [SciML Scientific Machine Learning ecosystem](https://sciml.ai/). It holds the sensitivity analysis utilities. Users interested in using this functionality should check out [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/). The project is written primarily in Julia, distributed under the Other license, first published in 2016. Key topics include: adjoint, backpropogation, dae, dde, differential-equations.
SciMLSensitivity.jl
SciMLSensitivity.jl is a component package in the SciML Scientific Machine Learning ecosystem.
It holds the sensitivity analysis utilities. Users interested in using this
functionality should check out DifferentialEquations.jl.
Tutorials and Documentation
For information on using the package,
see the stable documentation. Use the
in-development documentation for the version of
the documentation, which contains the unreleased features.
Contributors
Showing top 12 contributors by commit count.
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