NVIDIA/physicsnemo

NVIDIA/physicsnemo

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

Language: Python

License: Apache-2.0

Stars: 2911

Forks: 693

Open issues: 63

Created: 2023-01-26T20:34:45Z

Pushed: 2026-06-11T00:46:17Z

Default branch: main

Fork: no

Archived: no

README:

NVIDIA PhysicsNeMo

📝 NVIDIA PhysicsNeMo is undergoing an update to v2.0 - all the features, with easier installation and integration to external packages. See the migration guide for more details!

 

[NVIDIA PhysicsNeMo](#what-is-physicsnemo) | **Documentation** | [Install Guide](#installation) | [Getting Started](#getting-started-with-physicsnemo) | [Contributing Guidelines](#contributing-to-physicsnemo) | **Dev 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.

• [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)

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:

Component | Description | ---- | --- | **physicsnemo.models** ( More Details) | 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** | Optimized and scalable built-in data pipelines fine-tuned to handle engineering and scientific data structures like point clouds, meshes, etc.| **physicsnemo.distributed** | A distributed computing sub-module built on top of torch.distributed to enable parallel training with just a few steps| **physicsnemo.curator** | A sub-module to streamline and accelerate the process of data curation for engineering datasets| [physicsnemo.sym](docs/api/physicsnemo.sym.rst) | Symbolic PDE residual computation — define equations via SymPy and compute physics-informed losses with automatic spatial derivatives (install with pip install "nvidia-physicsnemo[sym]")|

For a complete list, refer to the PhysicsNeMo API documentation for PhysicsNeMo.

AI4Science Library

Usually, PhysicsNeMo is used either as:

• A complementary tool to PyTorch when exploring AI for SciML and AI4Science applications.
• A deep learning research platform that provides scale and optimal performance on

NVIDIA GPUs.

Domain-Specific Packages

The following are packages dedicated to domain experts of specific communities, catering to their unique exploration needs:

• PhysicsNeMo CFD: Inference sub-module of PhysicsNeMo

to enable CFD domain experts to explore, experiment, and validate using pretrained AI models for CFD use cases.

• PhysicsNeMo Curator: Inference sub-module

of PhysicsNeMo to streamline and accelerate the process of data curation for engineering datasets.

• Earth-2 Studio: Inference sub-module of PhysicsNeMo

to enable climate researchers and scientists to explore and experiment with pretrained AI models for weather and climate.

Scalable GPU-Optimized Training Library

PhysicsNeMo provides a highly optimized and scalable training library for maximizing the power of NVIDIA GPUs. Distributed computing utilities allow for efficient scaling from a single GPU to multi-node GPU clusters with a few lines of code, ensuring that large-scale physics-informed machine learning (ML) models can be trained quickly and effectively. The framework includes support for advanced optimization utilities, [tailor-made…