Ground-motion simulation via the Stochastic Method

Independent Hypothesis Weighting for multiple testing with side-information in Julia

A toolkit for the quantum and classical Dicke model in Julia.

Fast implementations of FastICA and DUET for blind source separation

Nippon color generator

-

Toolbox for categorical time-series analysis.

A framework to define and output observables and statistics from CliMA simulations

Generates a cellular automata animation of the given GitHub repository.

Drawing functions for `SimpleGraphs`

Repository for plotting implicitly defined surfaces and curves in R3.

Differentiable acoustic ray tracers

Interface-based REST library for Julia, HTTP.jl

Package for modeling glycolysis activity

Counterfactual Regret Minimization Game solution methods for Julia

A Julia package for meshes and basis functions of Method of Moments (MoM) and Multi-level Fast Multipole Algorithm (MLFMA).

A Julia library to resample timeseries (from TimeSeries.jl)

Perform image hashing in julia

Tools for creating, moving, and discretizing rigid bodies

A Julia package for modelling Algorithmic Recourse Dynamics.

Maps from the line into spaces

Algorithms for performing angular differential imaging (ADI)

Method to estimate phylogenetic networks from algebraic invariants

-

A Julia package that coverts (complex) tables into LaTeX code.

A Makie theme

A Julia package for reading and writing LAS data

Transformations for the SatelliteToolbox.jl ecosystem.

Package to parse Basis Sets from Basis Set Exchange API

Julia package for MPI reconstruction

-

Alpha stable and sub-Gaussian distributions in Julia

-

Experimental Approach to McCormick Relaxation Source-Code Transformation

-

Solutions for combinatorial problems using symbolic methods.

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)

Efficient RAG knowledge pack creator from online Julia documentation

Interfaces for deep learning data pipelines.

Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event