Climate Machine: an Earth System Model that automatically learns from data

Black-box optimization for Julia

Statistical plotting recipes for Plots.jl

Density-functional toolkit

Play atmospheric modelling like it's LEGO.

Combine ingredients for a plot

Probabilistic programming via source rewriting

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

A Julia package for fitting (statistical) mixed-effects models

Computational geometry in Julia

Molecular simulation in Julia

"Distributions" that might not add to one.

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

Geometric Deep Learning for Flux

Finite element toolbox for Julia

Probabilistic Programming with Gaussian processes in Julia

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.

Surrogate modeling and optimization for scientific machine learning (SciML)

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.

Computer vision models for Flux

Modeling and simulation of multidomain engineering systems

Simple curve fitting in Julia

A Julia package for Gaussian Processes

Lazy arrays and linear algebra in Julia

A Julia package to perform Bifurcation Analysis

Hypothesis tests for Julia

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

Julia package for kernel functions for machine learning

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

Julia package for automated Bayesian inference on a factor graph with reactive message passing

:rocket: Julia package for orthogonal polynomial transforms :snowboarder:

Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia

An intuitive modeling interface for infinite-dimensional optimization problems.

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem