https://github.com/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://github.com/sciml/scimlsensitivity.jl
adjoint backpropogation dae dde differential-equations differentialequations hacktoberfest neural-ode neural-sde ode scientific-machine-learning sciml sde sensitivity-analysis
Last synced: about 1 month ago
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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.
- Host: GitHub
- URL: https://github.com/sciml/scimlsensitivity.jl
- Owner: SciML
- License: other
- Created: 2016-11-02T17:38:07.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2025-08-20T11:17:46.000Z (about 2 months ago)
- Last Synced: 2025-08-29T21:43:00.805Z (about 1 month ago)
- Topics: adjoint, backpropogation, dae, dde, differential-equations, differentialequations, hacktoberfest, neural-ode, neural-sde, ode, scientific-machine-learning, sciml, sde, sensitivity-analysis
- Language: Julia
- Homepage: https://docs.sciml.ai/SciMLSensitivity/stable/
- Size: 89.7 MB
- Stars: 359
- Watchers: 18
- Forks: 78
- Open Issues: 118
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Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Citation: CITATION.bib