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.

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.

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Data structures in Julia to enable power systems analysis. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

Julia for optimization simulation and modeling of PowerSystems. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

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

Julia package to run Dynamic Power System simulations. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

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

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Gaussian Process package based on data augmentation, sparsity and natural gradients

Web server to run just the `@bind` parts of a Pluto.jl notebook

Easy benchmark tracking for packages

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

Hydrological modeling

A framework for simulating viscous incompressible flows about arbitrary body shapes.

Robust implementation for random-walk Metropolis-Hastings algorithms

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

Stochastic Optimization, Learning, Uncertainty and Sampling

High level functions for analyzing the output of simulations

Abstract types and interfaces for Markov chain Monte Carlo methods

Preheat your MCMC

Bayesian Generalized Linear models using `@formula` syntax.

Global glacier model using Universal Differential Equations for climate-glacier interactions

Turing + Makie = Turkie

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

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

-

-

Implementations of single and multi-ellipsoid nested sampling

Methods for computational information geometry

Common functions in actuarial and financial routines

Utility package for Sienna's simulation infrastructure

Examples of how to use the modeling capabilities developed under the Scalable Integrated Infrastructure Planning Initiative at NREL.

Comparing performance and results of mcmc options using Julia