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

Visualization for Flux.Chain neural networks

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An open source model predictive control package for Julia.

Flexible and efficient persistent homology computation.

Read R data files from 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)

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

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

Groebner bases in (almost) pure Julia

Rigorous function approximation using Taylor models in 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.

A Julia package for solving multi-objective optimization problems

A package for discrete-time optimal stochastic control

A General Approach to Seismic Inversion Problems using Automatic Differentiation

Atomic graph models for molecules and crystals in Julia

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

Julia package for solving singular integral equations

Basic numerical integration routines for presampled data.

Model and solve optimal control problems in Julia

Conic Augmented Lagrangian Interior-Point SOlver

Julia Kubernetes Client

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

Choo-choo

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

Utility package for subsetting, resampling, iteration, and partitioning of various types of data sets in Machine Learning

Multivariate polynomials implementation of commutative and non-commutative variables

Automated Bayesian model discovery for time series data

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

Implementation of various ensemble Kalman Filter data assimilation methods in Julia

Dithering algorithms in Julia.

Distributed Julia arrays using the MPI protocol

A gradient-based outer approximation solver for convex mixed-integer nonlinear programming (MINLP)

High-level, high-performance, constraint-based reconstruction and analysis in Julia

A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.

Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)

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.