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

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

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

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

Building dynamical systems compositionally

Base functionality library for QuantumOptics.jl

A collection of tools for chemometrics and machine learning written in Julia.

Visualization for Flux.Chain neural networks

A package for Gaussian random field generation in Julia

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

Flexible and efficient persistent homology computation.

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

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

Read R data files from Julia

An open source model predictive control package for Julia.

A package for performing Singular Spectrum Analysis (SSA) and time-series decomposition

Rigorous function approximation using Taylor models in Julia

Groebner bases in (almost) pure Julia

Darcy flow and reservoir simulator based on Jutul.jl

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

Pull data from Federal Reserve Economic Data (FRED) directly into Julia

A General Approach to Seismic Inversion Problems using Automatic Differentiation

Atomic graph models for molecules and crystals in Julia

Julia package for solving singular integral equations

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

A tool for package developers for working with artifacts.

Julia Kubernetes Client

Choo-choo

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

Conic Augmented Lagrangian Interior-Point SOlver

Automated Bayesian model discovery for time series data

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

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Use Optim to train Flux models and visualize loss landscapes

Compositional data analysis in Julia

Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.

A high functioning package detective.

Nonlinear optical pulse propagator

Functions to compute omnidirectional landscape connectivity using circuit theory and the Omniscape algorithm.