An interface to various automatic differentiation backends in Julia.

Implementations of the models from the Statistical Rethinking book with Turing.jl

Causal, Higher-Order, Probabilistic Programming

A solver for nonlinear programming

Automatic Finite Difference PDE solving with Julia SciML

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Reusable compiler infrastructure for Julia GPU backends.

Interior-point solver in pure Julia

Geophysical fluid dynamics pseudospectral solvers with Julia and FourierFlows.jl.

Linear Operators for Julia

A Julia framework for invertible neural networks

A Julia package for strided array views and efficient manipulations thereof

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

Package for Simulation, Tomography and Analysis of Quantum Computers

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Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

It slices, it dices, it splices!

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

Fermi quantum chemistry program

System Identification toolbox, compatible with ControlSystems.jl

The Base interface of the SciML ecosystem

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

Algorithms from circuit theory to predict connectivity in heterogeneous landscapes

A Julia package dedicated to simulating quantum many-body systems using Matrix Product States (MPS)

ODE integration using Taylor's method, and more, in Julia

Julia package for simulating Dynamics on Networks

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Automatic differentiation of implicit functions

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Solve non-linear HJB equations.

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

A Julia package for the creation, manipulation and analysis of the structure, dynamics and functions of multilayer graphs.

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Algebraic Multigrid in Julia

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

Causal.jl - A modeling and simulation framework adopting causal modeling approach.

State estimation, smoothing and parameter estimation using Kalman and particle filters.

Combinatorial optimization layers for machine learning pipelines

Equation-based modeling and simulations in Julia