The Julia implementation of the generalised hierarchical Gaussian filter

Conditional probability distributions powered by DistributionsAD.jl

Electrochemical reaction rate modeling and nonequilibrium phase maps (via AD)

SeaPearl's pool of examples

Differentiable tree-based models for tabular data

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Tetrahedral meshing of distance functions in Julia

A simple Julia library for generating SVGs suitable for AxiDraw and similar pen plotters.

Finite Element Module for Julia that focusses on gradient-robust discretisations and multiphysics problems

Load/save geospatial data compatible with the GeoStats.jl framework

Implementation of domain specific language (DSL) for probabilistic graphical models

Download and analyze NASA Precipitation Products from the Precipitation Measurement Mission

Graphical tools for Bayesian inference and posterior predictive checks

Flexible photometric transit curves with limb darkening

A tool for visualizing tensor network contractions.

GLMakie + Gtk4

A package to calibrate atmospheric turbulence and convection parameterizations using gradient-free ensemble Kalman methods

🎲 Parameter estimation for Oceananigans using EnsembleKalmanProcesses

MDToolbox.jl: A Julia package for molecular dynamics trajectories analysis and modeling of biomolecules

Provide all functions needed to work with augmented likelihoods (conditionally conjugate with Gaussians)

Set Programming with JuMP

Physics-Enhanced Regression for Initial Value Problems

Probabilistic inference using contraction of tensor networks

Spiral engraving effect

Easily optimize generic performance metrics in differentiable learning.

An multi-platform spiking neural network simulator

GeoInterface-based geometry operations

General Relativistic Orbital Dynamics in Julia

A framework (not only) for large-scale agent-based models

Lazy, structured, and efficient operations with kernel matrices.

MeshGraphNets.jl is a software package for the Julia programming language that provides an implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

From Semiconductor to TransistorLevel Modeling in Julia

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Julia package for multicellular modeling

Simple, blazing fast, transformer components.

Interface to the RobotLocomotion Drake Visualizer tool from Julia

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Nonlinear regression in Julia

📊 A Julia package that summarises tabular data in the REPL

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data