Binary library builder for Sundials for the SciML scientific machine learning open source software organization

It's Angular2 business in the front, and a Julia party in the back! It's scientific machine learning (SciML) for the web

A repository for the discussion of PDE tooling for scientific machine learning (SciML) and physics-informed machine learning

Backend for DiffEqOnline, a webapp for scientific machine learning (SciML)

The Kinyarwanda and Kirundi Languages Toolkit (KKLTK) is a Python package for Kinyarwanda and Kirundi languages processing. KKLTK currently provides the sets of stopwords for both languages and other preprocessing tools such as Kinyarwanda and Kirundi tokenizers will be added soon. KKLTK requires Python 3.0, 3.5, 3.6, 3.7, or 3.8.

A 30-minute showcase on the how and the why of neural differential equations.

A wrapper for the Python PyDSTool library for the SciML Scientific Machine Learning organization

Using TensorFlow for physics-informed neural networks for scientific machine learning (SciML)

Solvers for finite element discretizations of PDEs in the SciML scientific machine learning ecosystem

Wrappers for arrays to make broadcasted operations multithreaded and multiprocessed for high-performance scientific machine learning (SciML)

Solvers for Stokes-type equations and saddle-point problems for scientific machine learning (SciML)

Monte Carlo simulation routines for high-performance parallelization of differential equation solvers and scientific machine learning

A helper repository for diffeqpy to enable high-performance differential equation solving scientific machine learning (SciML) in Python

Saving and loading of JuliaDiffEq types for I/O of scientific machine learning (SciML)

The tools for proper interactions between ApproxFun.jl and DifferentialEquations.jl for pseudospectiral partial differential equation discretizations in scientific machine learning (SciML)

Library for common tools for solving PDEs with finite difference methods (FDM), finite volume methods (FVM), finite element methods (FEM), and psuedospectral methods in a way that integrates with the SciML Scientific Mechine Learning ecosystem

Webinar about state-of-the-art Machine Learning in Julia

Repository for the Control of Stochastic Quantum Dynamics with Differentiable Programming paper.

Simplified implementation of locally adaptive activation functions (LAAF) with slope recovery for deep and physics-informed neural networks (PINNs) in PyTorch.