Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Building and using pyramids for large raster data

Preheat your MCMC

ClimaAtmos.jl is a library for building atmospheric circulation models that is designed from the outset to leverage data assimilation and machine learning tools. We welcome contributions!

Kite and tether models for the simulation of kite power systems

Clima's Land Model

Clima's Land Model

ClimaCoupler: bringing atmosphere, land, and ocean together

Basic functionality for EarthSciML system

Models of gas-phase atmospheric chemistry and related processes

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A CPU- and GPU-friendly package for solving ordinary differential equations

Symbolic programming for the next generation of numerical software

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

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

Julia package to run Dynamic Power System simulations. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

High Level API Finite Element Methods based on ExtendableGrids and ExtendableFEMBase

Quantum dynamics simulation environment

Bayesian inference with probabilistic programming.

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 full stack simulator of quantum hardware, from the low-level analog physics to high-level network dynamics. Includes discrete event simulator, symbolic representation for quantum object, and works with many backend simulators.

EHT Image domain analysis through template matching.

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Controllers for airborne wind energy systems

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.

BOSS (Bayesian Optimization with Semiparametric Surrogate)

The Base interface of the SciML ecosystem

Phylogenetic Estimation of Shifts in the Tempo of Origination

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method

The Julia implementation of the generalised hierarchical Gaussian filter

A Julia package for behavioural modeling

Trixi.jl: Adaptive high-order numerical simulations of conservation laws in Julia

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

A modern, high-performance and comprehensive set of tools for transformations between any standard and user-defined reference frame.

ADRIA: Adaptive Dynamic Reef Intervention Algorithms. A multi-criteria decision support platform for informing reef restoration and adaptation interventions.

Julia implementation of the ISOKANN algorithm for the computation of invariant subspaces of Koopman operators

Julia package for simulating Dynamics on Networks