Variational quantum circuit simulator in Julia, under GPLv3

Unitful and differentiable gravitational N-body simulation code in Julia

YaoLang: The next DSL for Yao and quantum programs.

Generative Models with trainable conditional distributions in Julia!

A time domain electrical energy grid modeling and simulation tool with a focus on the control of power electronics converters

Neural Network based models for Natural Language Processing

Auto differentiation over linear algebras (a Zygote extension)

A turbulence-convection single column model cloud parameterization.

DPFEHM: A Differentiable Subsurface Flow Simulator

Learning state-space targets in dynamical systems

Pruning framework and methods for Flux

ParameterEstimation.jl is a Julia package for estimating parameters and initial conditions of ODE models given measurement data.

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Base package for the GeoStats.jl framework

Geospatial tables compatible with the GeoStats.jl framework

Three-dimensional panel method for low-speed aerodynamics

An easy to use Flux implementation of the Kolmogorov Arnold Network. This is a Julia version of TorchKAN.

Neural network training, fast and easy.

Your local Flux surgeon

Package for Bayesian and classical estimation and inference based on statistics that are filtered through a trained neural net

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

DINCAE (Data-Interpolating Convolutional Auto-Encoder) is a neural network to reconstruct missing data in satellite observations.

Load/save geospatial data compatible with the GeoStats.jl framework

Differentiable tree-based models for tabular data

SeaPearl's pool of examples

Electrochemical reaction rate modeling and nonequilibrium phase maps (via AD)

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

Maintainable type inference

Lazy, structured, and efficient operations with kernel matrices.

General Relativistic Orbital Dynamics in Julia

Easily optimize generic performance metrics in differentiable learning.

Nonlinear regression in Julia

Simple, blazing fast, transformer components.

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.

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From Semiconductor to TransistorLevel Modeling in Julia

Representing machine learning models using mathematical programming

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks

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