An implementation of ARMA convolutional layers in Julia.

Unitful and differentiable gravitational N-body simulation code in Julia

-

Starter kit for legendary models

Battery modelling using Jutul.jl and BattMo in MRST

A Julia package for Bayesian quantum state tomography

This julia package addresses the membership problem for local polytopes: it constructs Bell inequalities and local models in multipartite Bell scenarios with binary outcomes.

-

Simulate multivariate distributions with arbitrary marginals.

Benchmark BLAS Libraries

Benchmark BLAS libraries on GPUs

A package to calibrate atmospheric turbulence and convection parameterizations using gradient-free ensemble Kalman methods

Stochastic Optimization, Learning, Uncertainty and Sampling

The automatically differentiable and GPU-compatible toolkit for CMB analysis.

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

-

Parsers and processing for geoenergy data file formats in Julia.

GraphNetCore.jl is a software package for the Julia programming language that provides an the core functionality of the MeshGraphNets.jl package. Some parts are based on the implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

-

Modern Hopfield layer implementations in Julia

Fast and flexible glacier ice flow models

Methods for computational information geometry

Experimental framework for automatic differentiation finite-volume simulators

Darcy flow and reservoir simulator based on Jutul.jl

JutulDarcyRules: ChainRules extension to Jutul and JutulDarcy

Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.

Matrix Product Belief Propagation

Methods for the evaluation of maximum entropy distributions

Package for solving generalized BdG mean field theory of interacting systems.

MeshGraphNets.jl is a software package for the Julia programming language that provides an implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

A collection of microscopy tools

Compute the nearest correlation matrix using a pure Julia implementation

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

-

Global glacier model using Universal Differential Equations for climate-glacier interactions

-

Operator layers for Flux.jl that allow for the construction of Neural Operator models by using Flux's API. Useful for discretization-independent spatio-temporal ML models.

Finite Differencing Method in Julia language

Feynman's variational path-integral model for the Fröhlich polaron. Calculates temperature dependent polaron mobilities, and other polaron observables.

Fast, accurate, and flexible spectral analysis for compressible quantum fluids