Bayesian inference with probabilistic programming.

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Trixi.jl: Adaptive high-order numerical simulations of conservation laws in Julia

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

Robust robotic localization and mapping, together with NavAbility(TM). Reach out to info@wherewhen.ai for help.

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

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Proximal operators for nonsmooth optimization in Julia

For working with dimensions of arrays by name

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

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

A package for time series data processing, classification, clustering, and prediction.

Macros and functions to work with DSGE models.

A general-purpose toolbox for efficient Kronecker-based algebra.

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

High level functions for analyzing the output of simulations

Imputation methods for missing data in julia

Preheat your MCMC

Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

Structured optimization in Julia

Bayesian Generalized Linear models using `@formula` syntax.

Turing + Makie = Turkie

Robot Motion Estimate: Tools, Variables, and Factors for SLAM in robotics; also see Caesar.jl.

Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks

Frequency domain, multi-tone harmonic balance, simulation of scattering parameters and noise in nonlinear circuits containing Josephson junctions.

Lightweight robust covariance estimation in Julia

Methods for computational information geometry

Tools to analyze and manipulate climate (spatiotemporal) data. Also used by ClimateTools and ClimatePlots

Common functions in actuarial and financial routines

Comparing performance and results of mcmc options using Julia

Transformations for performing feature engineering in machine learning applications

Decompose a signal/timeseries into structure and noise or seasonal and residual components

Diagonalization procedures for Julia (PCA, Whitening, MCA, gMCA, CCA, gCCA, CSP, CSTP, AJD, mAJD)

High-level model-order reduction to automate the acceleration of large-scale simulations

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An interface for accelerated simulation of high-dimensional collisionless and electrostatic plasmas.