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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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Ito interpretation for SDE adjoints #279

Open frankschae opened 4 years ago

frankschae commented 4 years ago

2 times the standard correction term from Stratonovich to Ito must be added to the drift function to ensure that we get the proper backwards evolution in the Ito sense.

frankschae commented 3 years ago

Works for EM(). Higher order schemes have issues with the reversion of the trajectory, see https://github.com/SciML/DiffEqNoiseProcess.jl/pull/62