Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

Pocket Flow: Codebase to Tutorial

An elegant PyTorch deep reinforcement learning library.

Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.

A library for scientific machine learning and physics-informed learning

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

A differentiable PDE solving framework for machine learning

A simple and well styled PPO implementation. Based on my Medium series: https://medium.com/@eyyu/coding-ppo-from-scratch-with-pytorch-part-1-4-613dfc1b14c8.

Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch

Firedrake is an automated system for the portable solution of partial differential equations using the finite element method (FEM)

Differentiable Fluid Dynamics Package

PyTorch implementation of DDPG algorithm for continuous action reinforcement learning problem.

SmartSim Infrastructure Library.

An open-source Python platform of coupling deep reinforcement learning and OpenFOAM

ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equations.

Universal, autodiff-native software components for Simulation Intelligence. 📦

A multi-agent reinforcement learning environment to design and benchmark control strategies aimed at reducing drag in turbulent open channel flow

A synthetic, isotropic turbulence generator for constant density flows that enforces the discrete divergence-free condition.

A scalable reinforcement learning framework for CFD on HPC systems

A framework to design and develop reinforcement learning environments for single- and multi-physics active flow control.

A ML framework for the SOD2D CFD solver using SmartSim

CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms

Reinforcement Learning for Wind Farm Control