EasyHybrid.jl

Integrates neural networks with mechanistic models.

Powered by Lux.jl

Built for speed and flexibility in pure Julia. Native GPU acceleration across CUDA, AMDGPU, Metal, and Intel platforms enables seamless scaling from prototyping to production.

Seamless Data Handling

Built for efficient data manipulation in pure Julia. DataFrames.jl handles tabular data and AxisKeys.jl provides multi-dimensional named arrays with automatic differentiation support.

Feature Research

Using EasyHybrid in your research? Share your work with us through a pull request or drop us a line, and we'll showcase it here alongside other innovative applications.

How to Install EasyHybrid.jl?

Since EasyHybrid.jl is registered in the Julia General registry, it is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing add EasyHybrid. Alternatively, you can also do

julia

julia> using Pkg
julia> Pkg.add("EasyHybrid")

If you want to use the latest unreleased version of EasyHybrid.jl you can run the following command: (in most cases the released version will be same as the version on github)