Differentiable RayTracing in Julia

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

A Julia framework for invertible neural networks

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

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)

Convenience meta-package to load essential packages for statistics

ConstraintSolver in Julia: Blog posts ->

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

Fermi quantum chemistry program

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

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 algorithms for nonsmooth optimization in Julia

Proximal operators 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

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

Library for finding the roots of a function using interval arithmetic

Algorithms and data structures for game theory in Julia

Julia package for simulating Dynamics on Networks

Complex neural network examples for Flux.jl

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

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

Solve non-linear HJB equations.

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

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.

Algebraic Multigrid in Julia