Robust robotic localization and mapping, together with NavAbility(TM). Reach out to info@wherewhen.ai for help.

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.

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

An interface to various automatic differentiation backends in Julia.

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

Automatic Finite Difference PDE solving with Julia SciML

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

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

System Identification toolbox, compatible with ControlSystems.jl

The Base interface of the SciML ecosystem

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

Algorithms from circuit theory to predict connectivity in heterogeneous landscapes

ODE integration using Taylor's method, and more, in Julia

Julia package for simulating Dynamics on Networks

Automatic differentiation of implicit functions

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.

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Algebraic Multigrid in Julia

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

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

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

Combinatorial optimization layers for machine learning pipelines

Equation-based modeling and simulations in Julia

A standard library of components to model the world and beyond

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

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

Package for dynamical modeling of power grids

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

-

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

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.