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

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

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

A Julia interface to the FICO Xpress Optimization suite

A Julia/JuMP Package for Gas Network Optimization

A Julia interface to AMPL-enabled solvers

Building dynamical systems compositionally

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

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.

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An open source model predictive control package for Julia.

Rigorous function approximation using Taylor models in Julia

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

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

Darcy flow and reservoir simulator based on Jutul.jl

A Julia package for solving multi-objective optimization problems

A General Approach to Seismic Inversion Problems using Automatic Differentiation

Atomic graph models for molecules and crystals in Julia

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

A package for discrete-time optimal stochastic control

Model and solve optimal control problems in Julia

Choo-choo

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

Conic Augmented Lagrangian Interior-Point SOlver

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

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

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

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

Implementation of various ensemble Kalman Filter data assimilation methods in Julia

Automated Bayesian model discovery for time series data

Use Optim to train Flux models and visualize loss landscapes

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

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.

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

A package containing a library of moist thermodynamic relations.

Nonlinear optical pulse propagator

A Power Flow Solver for GPUs in Julia

A package containing a library of moist thermodynamic relations.