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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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.

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.

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.

Methods for computational information geometry

Common functions in actuarial and financial routines

Transformations for performing feature engineering in machine learning applications

Comparing performance and results of mcmc options using Julia

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

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

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Fast approximate high-dimensional hierarchical Bayesian inference

Composable contracts, models, and functions that allow for modeling of both simple and complex financial instruments

Octofitter is a Julia package for performing Bayesian inference against direct images of exoplanets, relative astrometry, and astrometric acceleration of the host star.

Differential equations on manifolds

Uncertainty Quantification in Julia

Initializes and sets up reference elements and physical meshes for DG.

Apply meaningful keys and custom behavior to indices.