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.

Convex optimization for fun and profit. (Now in Julia!)

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

Bayesian Generalized Linear models using `@formula` syntax.

Turing + Makie = Turkie

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

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

Exploring the computational power of fermionic quantum systems. Ab initio computation and basis set optimization for electronic structure problems.

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.

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

The Julia implementation of the generalised hierarchical Gaussian filter

Easily optimize generic performance metrics in differentiable learning.

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Differentiable JUlia ICE model

EHT Image domain analysis through template matching.

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Building and using pyramids for large raster data

Lightweight module of neural differential equations in Kinetic.jl

A Julia package for behavioural modeling

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Optimal experimental design of ODE and DAE systems in julia

Gradient Ascent Pulse Engineering in Julia

Ship maneuvering simulation tool with respect to ShipMMG model

A Julia implementation of estimation and validation algorithms for time series compatible with incomplete data.

Geometry optimization for molecular simulation

Symmetry-allowed k ⋅ p expansions

Julia Package for Active Inference

Bayesian Safety Signal Detection in Julia