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Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
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High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning
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开发工具Julia · PythonGitHub @SciML自行安装运营者未确认
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High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
翻译准备中
取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
OrdinaryDiffEq.jl is a component package in the DifferentialEquations ecosystem. It holds the
ordinary differential equation solvers and utilities. While completely independent
and usable on its own, users interested in using this
functionality should check out DifferentialEquations.jl.
Assuming that you already have Julia correctly installed, it suffices to import
OrdinaryDiffEq.jl in the standard way:
OrdinaryDiffEq v7 bumps to SciMLBase v3 and RecursiveArrayTools v4 with breaking changes across all sublibraries. See NEWS.md for the full migration guide.查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
OrdinaryDiffEq.jl is a component package in the DifferentialEquations ecosystem. It holds the
ordinary differential equation solvers and utilities. While completely independent
and usable on its own, users interested in using this
functionality should check out DifferentialEquations.jl.
Assuming that you already have Julia correctly installed, it suffices to import
OrdinaryDiffEq.jl in the standard way:
OrdinaryDiffEq v7 bumps to SciMLBase v3 and RecursiveArrayTools v4 with breaking changes across all sublibraries. See NEWS.md for the full migration guide.查看完整 README ↗