Integrated Assessment Modeling Framework

Basic Geometry Types

Extended precision math, accurate and performant

Symbolic programming for the next generation of numerical software

Computational geometry in Julia

Symbolic expressions, rewriting and simplification

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

Award winning software library for nonlinear dynamics and nonlinear timeseries analysis

A comprehensive open source computer algebra system for computations in algebra, geometry, and number theory.

Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

A Julia package for large-scale tensor computations, with a hint of category theory

Groebner bases in (almost) pure Julia

Implementation of variational Bayes inference algorithms

Solve non-linear HJB equations.

LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.

Root finding functions for Julia

The Base interface of the SciML ecosystem

Basic interface for manifolds in Julia

Financial market technical analysis & indicators in Julia

GAP packages for Julia integration

Combine ingredients for a plot

CEED Library: Code for Efficient Extensible Discretizations

Julia package for simulating Dynamics on Networks

Filter design, periodograms, window functions, and other digital signal processing functionality

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

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)

Arrays with arbitrarily nested named components.

Generic numerical linear algebra in Julia

Generic numerical linear algebra in Julia

Make available to Julia the sparse functionality in MKL

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Forward Mode Automatic Differentiation for Julia

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

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.

Julia bindings to the FFTW library for fast Fourier transforms

Iterative algorithms for solving linear systems, eigensystems, and singular value problems

Symbolic parser for Julia language term rewriting using REDUCE algebra