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diffeqbase.jl

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

diffeqcallbacks.jl

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

differentiableeigen.jl

The current implementation of `LinearAlgebra.eigen` does not support sensitivities. DifferentiableEigen.jl offers an `eigen` function that is differentiable by every AD-framework with support for ChainRulesCore.jl or ForwardDiff.jl.

distributedhyperopt.jl

DistributedHyperOpt.jl is a package similar to HyperOpt.jl, but explicitly focusing on distributed (multi-processing) hyperparameter optimization by design.

easyfit.jl

Easy interface for obtaining fits for 2D data

fmi-cross-check

Results and FMUs for the FMI Cross-Check

fmi-standard.org

Website of the Functional Mock-Up Interface (FMI)

fmi.jl

FMI.jl is a free-to-use software library for the Julia programming language which integrates FMI (fmi-standard.org): load or create, parameterize, differentiate and simulate FMUs seamlessly inside the Julia programming language!

fmibase.jl

FMIBase.jl provides the foundation for the Julia packages FMIImport.jl and FMIExport.jl.

fmibuild.jl

FMIBuild.jl holds dependencies that are required to compile and zip a Functional Mock-Up Unit (FMU) compliant to the FMI-standard (fmi-standard.org). Because this dependencies should not be part of the compiled FMU, they are out-sourced into this package. FMIBuild.jl provides the build-commands for the Julia package FMIExport.jl.

fmicore.jl

FMICore.jl implements the low-level equivalents of the C-functions and C-data types of the FMI-standard (fmi-standard.org) for the Julia programming language.

fmiexport.jl

FMIExport.jl is a free-to-use software library for the Julia programming language which allows for the export of FMUs (fmi-standard.org) from any Julia-Code. FMIExport.jl is completely integrated into FMI.jl.

fmiflux.jl

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to place FMUs (fmi-standard.org) everywhere inside of your ML topologies and still keep the resulting model trainable with a standard (or custom) FluxML training process.

fmiimport.jl

FMIImport.jl implements the import functionalities of the FMI-standard (fmi-standard.org) for the Julia programming language. FMIImport.jl provides the foundation for the Julia packages FMI.jl and FMIFlux.jl.

fmisensitivity.jl

Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event

fmizoo.jl

FMIZoo.jl is a collection of testing and example FMUs (fmi-standard.org) for the Julia libraries FMI.jl and FMIFlux.jl.

forwarddiffchainrules.jl

hudade.jl

Mixed Hybrid, Universal, Discrete, Algebraic and (Ordinary) Differential Equations in the Julia programming language.

numericalintegration.jl

Basic numerical integration routines for presampled data.

reversediff.jl

Reverse Mode Automatic Differentiation for Julia

scimlbase.jl

The Base interface of the SciML ecosystem

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.

vvuq-framework