Differentiable RayTracing in Julia

A Julia framework for invertible neural networks

Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.

Finite Element tools in Julia

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

A development environment for robust and global optimization

A Julia/JuMP Package for Unbalanced Power Network Optimization

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Interior point solver for general convex conic optimization problems

Convenience meta-package to load essential packages for statistics

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)

ConstraintSolver in Julia: Blog posts ->

Fermi quantum chemistry program

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

Gaussian Process package based on data augmentation, sparsity and natural gradients

A Julia interface to the CPLEX solver

System Identification toolbox, compatible with ControlSystems.jl

A solver for mixed-integer convex optimization

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia

Proximal operators for nonsmooth optimization in Julia

Proximal algorithms for nonsmooth optimization in Julia

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

Library for finding the roots of a function using interval arithmetic

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

Algorithms and data structures for game theory in Julia

Julia package for simulating Dynamics on Networks

Complex neural network examples for Flux.jl

Differentiating convex optimization programs w.r.t. program parameters

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

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.

A Julia implementation of basic tools for time series analysis compatible with incomplete data.

Arrays which also have a label for each element for easy scientific machine learning (SciML)

A flexible neural net training library inspired by fast.ai

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Perceptually Uniform Colour Maps for Julia