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SciML / 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.
https://docs.sciml.ai/SciMLSensitivity/stable/
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Support derivatives with respect to `u0` in the forward modes #156

Open ChrisRackauckas opened 4 years ago

ChrisRackauckas commented 4 years ago

This isn't too hard: set the initial conditions in the forward propagation or use a larger set of dual numbers in the ForwardDiff form. It just needs to get setup.

ChrisRackauckas commented 3 years ago

https://github.com/SciML/DiffEqSensitivity.jl/pull/426 had to be specialized to not do forward mode with no parameters.

ChrisRackauckas commented 3 years ago

ForwardDiffSensitivity was handled in https://github.com/SciML/DiffEqSensitivity.jl/pull/428