Bayesian inference with probabilistic programming.

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

Symbolic programming for the next generation of numerical software

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

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

Julia Animations and Visualizations

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.

Julia package for function approximation

An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

Probabilistic programming via source rewriting

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

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

"Distributions" that might not add to one.

Visualization of Julia profiling data

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Named dimensions and indexing for julia arrays and other data

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Julia package for automated Bayesian inference on a factor graph with reactive message passing

Grid-based simulations in Julia

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

Raster manipulation for the Julia language

Performant arrays where each dimension can have a named axis with values

A Bayesian Analysis Toolkit in Julia

Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method

This library uses the LaTeX package pgfplots to produce plots.

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

Fast, general-purpose interactive applications for complex systems

Geographical plotting utilities for Makie.jl

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

Automatic Finite Difference PDE solving with Julia SciML

Differentiable RayTracing in Julia

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Interactive display of images and movies

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

Perceptually Uniform Colour Maps for Julia