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

Lightweight module for fusing physical and neural models

High Level API Finite Element Methods based on ExtendableGrids and ExtendableFEMBase

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Integrate deep neural network and reverse mode automatic differentiation into Gauss process, have fun !

Bayesian quantile regression in Julia

Population dynamics across space, in Julia.

This package models the dynamics of a biofilm using the Julia programming language.

A Julia package to provide RML imaging methods for high-resolution radio interferometry

Models of gas-phase atmospheric chemistry and related processes

Deep and convolutional neural networks with feedback operations in Flux.

Generate nice reports from MCMC-based approximations

Power spectrum inference of irregularly sampled time series using Gaussian Processes in Julia

MCMCDebugging.jl: debugging utilities for MCMC samplers

One package to train them all

Joint parameter and state inference for state space models using Bayesian techniques

Monotone Cubic B-Splines (arXiv:2307.01748)

Analytic routines for power system simulation results in the Sienna ecosystem

Dynamical systems identification

Advanced process control for AutomationLabs

Sole interface for trees trained via JuliaAI/DecisionTree.jl.

Tiny Gibbs sampler making use of AbstractMCMC.jl and MCMCChains.jl

Invariant Causal Prediction in pure Julia

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Fitting astrophysical star formation histories via CMD modelling.

Julia implementation of maximum entropy graph models

Operator pack for Jets.jl. Part of the COFII framework.

Computational models of algorithmic competition

A collection of background subtraction algorithms for spectroscopic data

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A package for Hidden Markov Models with exogenous control variables.

Multimodal datasets for Machine-Learning

Non-critical functions for SeaPearl

Basic structures for finite elements based on ExtendableGrids infrastructure

Julia wrapper for Flow*

Basic functionality for EarthSciML system

Scalable and distributed matrix-free abstractions for machine learning and scientific computing

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Data driven acoustic propagation models