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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
| Date | Stars |
|---|---|
| 2026-07-24 | 3087 |
| 2026-07-25 | 3089 |
| 2026-07-28 | 3089 |
| 2026-07-30 | 3089 |
| 2026-07-31 | 3108 |
| 2026-08-06 | 3129 |
Today
+21 stars today
This week
+40 stars this week
This month
— stars this month
Momentum
139.0
growth rate 1.29%/day
# NVIDIA PhysicsNeMo
<!-- markdownlint-disable -->
📝 NVIDIA PhysicsNeMo is undergoing an update to v2.0 - all the features, with easier installation and integration to external packages. See the [migration guide](https://github.com/NVIDIA/physicsnemo/blob/main/v2.0-MIGRATION-GUIDE.md) for more details!
[](https://www.repostatus.org/#active)
[](https://github.com/NVIDIA/physicsnemo/blob/master/LICENSE.txt)
[](https://github.com/psf/black)
[](https://github.com/NVIDIA/physicsnemo/actions/workflows/install-ci.yml)
[](https://app.codecov.io/gh/NVIDIA/physicsnemo)
<!-- markdownlint-enable -->
[**NVIDIA PhysicsNeMo**](#what-is-physicsnemo)
| [**Documentation**](https://docs.nvidia.com/deeplearning/physicsnemo/physicsnemo-core/index.html)
| [**Install Guide**](#installation)
| [**Getting Started**](#getting-started-with-physicsnemo)
| [**Contributing Guidelines**](#contributing-to-physicsnemo)
| [**Dev blog**](https://nvidia.github.io/physicsnemo/blog/)
## What is PhysicsNeMo?
NVIDIA PhysicsNeMo is an open-source deep-learning framework for building, training,
fine-tuning, and inferring Physics AI models using state-of-the-art SciML methods for
AI4Science and engineering.
PhysicsNeMo provides Python modules to compose scalable and optimized training and
inference pipelines to explore, develop, validate, and deploy AI models that combine
physics knowledge with data, enabling real-time predictions.
Whether you are exploring the use of neural operators, GNNs, or transformers, or are
interested in Physics-Informed Neural Networks or a hybrid approach in between, PhysicsNeMo
provides you with an optimized stack that will enable you to train your models at scale.
<!-- markdownlint-disable -->
<p align="center">
<img src=https://raw.githubusercontent.com/NVIDIA/physicsnemo/main/docs/img/value_prop/Knowledge_guided_models.gif alt="PhysicsNeMo"/>
</p>
<!-- markdownlint-enable -->
<!-- toc -->
- [More About PhysicsNeMo](#more-about-physicsnemo)
- [Scalable GPU-Optimized Training Library](#scalable-gpu-optimized-training-library)
- [A Suite of Physics-Informed ML Models](#a-suite-of-physics-informed-ml-models)
- [Seamless PyTorch Integration](#seamless-pytorch-integration)
- [Easy Customization and Extension](#easy-customization-and-extension)
- [AI4Science Library](#ai4science-library)
- [Domain-Specific Packages](#domain-specific-packages)
- [Who is Using and Contributing to PhysicsNeMo](#who-is-using-and-contributing-to-physicsnemo)
- [Why Use PhysicsNeMo](#why-are-they-using-physicsnemo)
- [Getting Started](#getting-started-with-physicsnemo)
- [Resources](#resources)
- [Installation](#installation)
- [Contributing](#contributing-to-physicsnemo)
- [Communication](#communication)
- [License](#license)
<!-- tocstop -->
## More About PhysicsNeMo
At a granular level, PhysicsNeMo is developed as modular functionality and therefore
provides built-in composable modules that are packaged into a few key components:
<!-- markdownlint-disable -->
Component | Description |
---- | --- |
[**physicsnemo.models**](https://docs.nvidia.com/physicsnemo/latest/user-guide/model_architectures.html) ( [More Details](https://docs.nvidia.com/physicsnemo/latest/physicsnemo/api_models.html)) | A collection of optimized, customizable, and easy-to-use families of model architectures such as Neural Operators, Graph Neural Networks, Diffusion models, Transformer models, and many more|
[**physicsnemo.datapipes**](https://docs.nvidia.com/deeplearning/physExcerpt of 27,392 characters
Read on GitHub
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Peter Sharpe · @NVIDIA · United States
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Alexey Kamenev · NVIDIA · United States
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Peter Harrington · NVIDIA
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Nicholas Geneva · NVIDIA · United States
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Akshay Subramaniam · United States
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Mehdi Ataei · @NVIDIA · Canada
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Tao Ge
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:52842abb6a74338d, topic:deep-learning, topic:pytorch, desc:deep learning framework