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Simulating quantum computing verification

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

(Experimental) Predicting hand assignments in piano MIDI using neural networks

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ParticleHolography.jl: Holographic particle measurement in Julia.

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A Julia package for fusing broadcast expressions into optimized kernels

MPS and MPO methods based on ITensor (ITensors.jl)

Generate checklists and flow diagrams based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)

Tensor networks for quantum error correction.

Differentiable tree-based models for tabular data

Julia bindings for VkFFT

Welcome to Peridynamic Laboratory (PeriLab), a powerful software solution designed for tackling Peridynamic problems.

A small Julia package to facilitate working with the Trillion Dollar Words dataset.

Representing machine learning models using mathematical programming

A Tensor Network Library (TenNetLib.jl) built on top of ITensors.jl for quantum many-body problems.

FFT solvers in Julia language

Finite Differencing Method in Julia language

Quantum Exact Simulation Toolkit is a high performance simulator of quantum circuits, state-vectors and density matrices

Julia package for Light Based Tomographic Volumetric Additive Manufacturing.

Simulating Rydberg atom dynamics with Tensor Networks

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GraphNetCore.jl is a software package for the Julia programming language that provides an the core functionality of the MeshGraphNets.jl package. Some parts are based on the implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

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.

Various closures models (closure -> seal -> seal 🦭 -> walrus) for ocean flavoured fluid dynamics

ASDF, the Advanced Scientific Data Format

Deep-learning sliding window attention with circular boundary conditions. No tokenization, no patchify-ing.