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

Solve non-linear HJB equations.

Arrays which also have a label for each element for easy scientific machine learning (SciML)

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Algebraic Multigrid in Julia

Divide and Conquer Linear Algebra

Perceptually Uniform Colour Maps for Julia

Climate science package for Julia

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

Causal.jl - A modeling and simulation framework adopting causal modeling approach.

Roll a window over data; apply a function over the window.

Hydrological modeling

State estimation, smoothing and parameter estimation using Kalman and particle filters.

A standard library of components to model the world and beyond

Equation-based modeling and simulations in Julia

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.

Exploratory analysis of Bayesian models with Julia

Starter kit for legendary models

Package for dynamical modeling of power grids

Fast contact-implicit model predictive control for robotic systems that make and break contact with their environments.

MIRT: Michigan Image Reconstruction Toolbox (Julia version)

MADS: Model Analysis & Decision Support

MagNav: airborne Magnetic anomaly Navigation

Yet Another XArray-like Julia package

Julia implementations of multidimensional array convolution and nonlinear stencil operations

High-performance reactive message-passing based Bayesian inference engine

Dynamic Time Warping (DTW) and related algorithms in Julia, at Julia speeds

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

Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.

Macros and functions to work with DSGE models.

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

A Julia library of summation-by-parts (SBP) operators used in finite difference, Fourier pseudospectral, continuous Galerkin, and discontinuous Galerkin methods to get provably stable semidiscretizations, paying special attention to boundary conditions.

A helper tool for visualizing and editing a ModelingToolkit.jl system connections

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.