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.

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

🏔️Manopt. jl – Optimization on Manifolds in Julia

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

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

High level functions for analyzing the output of simulations

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

Methods for computational information geometry

Common functions in actuarial and financial routines

Comparing performance and results of mcmc options using Julia

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

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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

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

Abstraction layer for spanning factor graphs over various technologies

The Julia implementation of the generalised hierarchical Gaussian filter

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Lie groups and algebra with some quaternions

Differentiable JUlia ICE model

The base package for Optimization.jl, containing the structs and basic functions for it.

Symbolics-based function property propagation for optimization

EHT Image domain analysis through template matching.

Building and using pyramids for large raster data

Approximate the product between infinite functional objects on a manifold -- i.e. belief products

A Julia package for behavioural modeling

Lightweight module of neural differential equations in Kinetic.jl

Optimal experimental design of ODE and DAE systems in julia

Differentiation on manifolds