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Lux layers and utilities to build and train hybrid dynamic models.
Run Windows apps on 🐧 Linux with ✨ seamless integration
A comprehensive collection of 25+ recurrent neural network layers for Lux.jl
RDKitMinimalLib wrapper for the Julia programming language
A machine-learning excess Gibbs energy model that enables the prediction of phase equilibria (VLE and LLE) in multi-component mixtures.
Differentiable simulation of batteries in Julia using Jutul.jl
Interactive data visualizations and plotting in Julia
A markup-based typesetting system that is powerful and easy to learn.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Accelerate your ML research using pre-built Deep Learning Models with Lux
EntropyScaling.jl provides methods for modeling transport properties (viscosity, thermal conductivity, diffusion coefficients) based on entropy scaling and the Chapman-Enskog theory.
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Julia bindings for the Enzyme automatic differentiator
A Julia based simulation framework for thermodynamic cycles.
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials …
Graph-based molecule modeling toolkit for cheminformatics
Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Julia implementation of the Kolmogorov-Arnold network with custom gradients for fast training.
Distributed High-Performance Symbolic Regression in Julia
Taylor-mode automatic differentiation for higher-order derivatives
📦 The official Nextcloud installation method. Provides easy deployment and maintenance with most features included in this one Nextcloud instance.
A GNN model for the prediction of pure component vapor pressures.