Stars
Beginner, advanced, expert level Rust training material
Proteomics search & quantification so fast that it feels like magic
a programming library with geometric algorithms
Code for the book "The Elements of Differentiable Programming".
Python implementation of surface mesh resampling algorithm ACVD
The Node-Based Python Tool is a software tool that allows you to organize and work with reusable Python code in a dynamic network. Its intuitive GUI and modular approach can streamline your Python …
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.
High-performance automatic differentiation of LLVM and MLIR.
Tensors and dynamic neural networks in pure Rust.
Causal Discovery in Python. Learning causality from data.
Normalizing-flow enhanced sampling package for probabilistic inference in Jax
Yazi and Zellij with smart defaults & awesome plugins give helix/nvim a powerful yazi sidebar, git integrations, a configurable popup system (lazygit, a config ui, etc), zoxide integrations, zjstat…
A Python framework for GPU-accelerated simulation, robotics, and machine learning.
Lightweight, general, scalable C++ library for finite element methods
Coupling Code for Numerical Tools. The documentation can be found at
CoFEA Initiative aims to popularise free FE simulation codes
Differentiable interface to FEniCS/Firedrake for JAX using dolfin-adjoint/pyadjoint
Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks (ICML 2023)
Learning optimal wavelet bases using a neural network approach in Pytorch
Differentiable Finite Element Method with JAX
Interactive Jupyter Notebooks for learning the fundamentals of Density-Functional Theory (DFT)
This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means of neural networks using TensorFlow.