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juliahub.com : SciMLSensitivity
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.
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purl: pkg:julia/SciMLSensitivity
Keywords: sde, backpropogation, scientific-machine-learning, ode, differential-equations, neural-ode, sciml, dde, dae, adjoint, sensitivity-analysis, differentialequations, neural-sde, hacktoberfest
License: MIT
Latest release: about 2 months ago
First release: over 1 year ago
Dependent packages: 11
Stars: 306 on GitHub
Forks: 66 on GitHub
Total Commits: 2374
Committers: 63
Average commits per author: 37.683
Development Distribution Score (DDS): 0.525
More commit stats: commits.ecosyste.ms
See more repository details: repos.ecosyste.ms
Funding links: https://github.com/sponsors/SciML
Last synced: about 16 hours ago