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

Efficient data manipulation with DataFrames.jl for tabular data, and DimensionalData.jl and AxisKeys.jl for named multidimensional 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.

Install via Julia Package Manager

EasyHybrid.jl is registered in the Julia General registry. Enter the package manager by pressing ] in the REPL and add it with a single command.

Learn more

julia

julia> ]
pkg> add EasyHybrid

Install via Pkg.add

Use Julia's Pkg module directly to add EasyHybrid.jl programmatically, great for reproducible project setups.

Learn more

julia

using Pkg
Pkg.add("EasyHybrid")

Install Latest from GitHub

Install directly from GitHub to get the latest unreleased version. In most cases this will match the released version.

GitHub repo

julia

using Pkg
Pkg.add(url="https://github.com/EarthyScience/EasyHybrid.jl")
# or in the REPL
julia> ]
pkg> add EasyHybrid#main

Load EasyHybrid in Your Session

Once installed, add this line at the beginning of any Julia script or session to load EasyHybrid.jl and access all its features.

Learn more

julia

using EasyHybrid