Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

High-performance simulations of the Porous Electrode Theory for Li-ion batteries

A Julia interface to the FICO Xpress Optimization suite

A Julia/JuMP Package for Gas Network Optimization

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

A Julia interface to AMPL-enabled solvers

An open-source framework written in Julia for global integrated assessment models.

A package for Gaussian random field generation in Julia

Visualization for Flux.Chain neural networks

Robot Motion Estimate: Tools, Variables, and Factors for SLAM in robotics; also see Caesar.jl.

Multistart optimization methods in Julia.

Base functionality library for QuantumOptics.jl

An open source model predictive control package for Julia.

Calculate rigorously the feasible region for a set of real-valued inequalities with Julia

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

-

Rigorous function approximation using Taylor models in Julia

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

-

Flexible and efficient persistent homology computation.

Basic numerical integration routines for presampled data.

Julia package for solving singular integral equations

A package for discrete-time optimal stochastic control

Model and solve optimal control problems in Julia

CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.

Delaunay triangulations and Voronoi tessellations in two dimensions.

Darcy flow and reservoir simulator based on Jutul.jl

Atomic graph models for molecules and crystals in Julia

A General Approach to Seismic Inversion Problems using Automatic Differentiation

A Julia package for solving multi-objective optimization problems

Choo-choo

MultiJuMP enables the user to easily run multiobjective optimisation problems and generate Pareto fronts.

Graph IO functionality for various formats.

Conic Augmented Lagrangian Interior-Point SOlver

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

Implementation of various ensemble Kalman Filter data assimilation methods in Julia

Import, process and interpret geophysical data sets to be used in numerical models.

Distributed Julia arrays using the MPI protocol

Dithering algorithms in Julia.

Automated Bayesian model discovery for time series data