Bring Your Own Stack

An intuitive modeling interface for infinite-dimensional optimization problems.

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

The slow descent into madness

LAPACK development repository

Readily pin Julia threads to CPU-threads

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

Update immutable data

Types with default field values, keyword constructors and (un-)pack macros

Efficient Astronomical Time transformations in Julia.

A Modern Binary Ephemeris Reader for Julia, in Julia.

A python-based end-to-end testing package that will be able to test orbit prediction accuracy for Open Source Orbit Integrators

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

An open-source community LSST Solar System Simulator

Compiles Julia code to a standalone library (experimental)

Transformations for the SatelliteToolbox.jl ecosystem.

Astronomical time keeping in Julia

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Makes Julia reason with equations. General purpose metaprogramming, symbolic computation and algebraic equational reasoning library for the Julia programming language: E-Graphs & equality saturatio…

Code accompanying my blog post: So, what is a physics-informed neural network?

Read, write, and interact with data in the Digital RF and Digital Metadata formats

SMILI: Sparse Modeling Imaging Library for Interferometry

SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

Symbolic programming for the next generation of numerical software

Intro to Julius Graph