A Julia based simulation framework for thermodynamic cycles using CoolProp, Clapyeron and ModelingToolkit

Boundary value problem (BVP) solvers for scientific machine learning (SciML)

Sampling from intractable distributions, with support for distributed and parallel methods

Robust implementation for random-walk Metropolis-Hastings algorithms

Linear Operators for Julia

A domain specific language (DSL) for probabilistic graphical models

Random Integrators for many-body quantum systems

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Julia package for PEPS algorithms

Julia bindings for the FLINT number theory C library

Neural Network primitives with multiple backends

Implementation of normalising flows and constrained random variable transformations

Parallel distributed-memory version of Gridap

BlockArrays for Julia

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

Solid state detector field and charge drift simulation in Julia

Julia package for tensor contractions and related operations

A data structure for mathematical optimization problems

One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs…

A Julia package for large-scale tensor computations, with a hint of category theory

Abstract types and methods for Gaussian Processes.

A Julia package dedicated to simulating quantum many-body systems using Matrix Product States (MPS)

✨ The Next Gen Airtable Alternative: No-Code Postgres

Progress meter for long-running computations

Statically sized arrays for Julia

Generic abstract algebra functionality in pure Julia (no C dependencies)

The NVIDIA GPU driver container allows the provisioning of the NVIDIA driver through the use of containers.

Grid-based approximation of partial differential equations in Julia

A Julia Basket of Hand-Picked Krylov Methods