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)

Fast low-rank matrix approximation in Julia

This is a Julia package of nonlinear solvers. These codes are used in my book, Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia.

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

A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.

Library for finding the roots of a function using interval arithmetic

The Base interface of the SciML ecosystem

Pure-Julia multidimensional h-adaptive integration

CEED Library: Code for Efficient Extensible Discretizations

Financial market technical analysis & indicators in Julia

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Fast jacobian computation through sparsity exploitation and matrix coloring

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.

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

:rocket: Julia package for orthogonal polynomial transforms :snowboarder:

Adaptive 1d numerical Gauss–Kronrod integration in Julia

High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)

High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)

Julia package for Gaussian quadrature

A Julia package to perform Bifurcation Analysis

A Julia package for defining and working with linear maps, also known as linear transformations or linear operators acting on vectors. The only requirement for a LinearMap is that it can act on a vector (by multiplication) efficiently.

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

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

Fast, continuous interpolation of discrete datasets in Julia

Grid-based approximation of partial differential equations in Julia