PhysicsNeMo CFD

PhysicsNeMo CFD | Getting started | Contributing Guidelines | Communication

What is PhysicsNeMo CFD?

NVIDIA PhysicsNeMo-CFD is a sub-module of NVIDIA PhysicsNeMo framework that provides the tools needed to integrate pretrained AI models into engineering and CFD workflows.

The library is a collection of loosely-coupled workflows around the trained AI models for CFD, with abstractions and relevant data structures.

Refer to the PhysicsNeMo framework to learn more about the full stack.

The library offers utilities for:

• NIM Inference:

  • An inference recipe calling a pre-trained AI models that were trained using PhysicsNeMo and hosted as NVIDIA Inference Microservices (for example, the DoMINO Automotive Aerodynamics NIM) from a Python interface, facilitating scalable deployment of trained models.

• Benchmarking of ML Model Accuracy:

  • A benchmark for evaluating and validating the results of trained ML models against traditional CFD results using broad set of built-in engineering metrics (for example, pointwise errors, integrated quantities, spectral metrics, PDE residuals). Related publication
  • Utilities to extend and build custom metrics, analyze, and visulaize the results of trained ML model, both mesh-based and point-cloud based models

• Hybrid Initialization:

  • An end-to-end recipe illustrating initializing a CFD simulation with a trained ML model hybridized with potential flow solutions, to accelerate CFD convergence (particularly for high-fidelity, unsteady cases). Related publication

Installation

PhysicsNeMo-CFD is a Python package that depends on the NVIDIA PhysicsNeMo framework.

For maximum cross-platform compatibility, we recommend using the PhysicsNeMo Docker container. Steps to use PhysicsNeMo container can be found in the Getting Started.

You can install PhysicsNeMo-CFD via pip:

git clone https://github.com/NVIDIA/physicsnemo-cfd.git
cd physicsnemo-cfd
pip install .

To get access to GPU accelerated functionalities from this repo when installing in a conda or a custom python environment please run below commands.

If you are using the PhysicsNeMo container, the GPU specific dependencies are pre-installed, hence this additional step is not required.

pip install .[gpu] --extra-index-url=https://pypi.nvidia.com

[!Note] PhysicsNeMo-CFD is an experimental library and currently v0; expect breaking changes. PhysicsNeMo-CFD is for demonstrating workflows, rather than providing a stable API for production-level deployments.

When updating, see the latest changes in the CHANGELOG.md file.

Getting started

To get started use the DoMINO NIM on a sample as shown below:

from physicsnemo.cfd.inference.domino_nim import call_domino_nim
import subprocess

filenames = [
    "drivaer_202.stl",
]
urls = [
    "https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_202/drivaer_202.stl",
]

for url, filename in zip(urls, filenames):
    subprocess.run(["wget", url, "-O", filename], check=True)

output_dict = call_domino_nim(
    stl_path="./drivaer_202.stl",
    inference_api_url="http://localhost:8000/v1/infer",
    data={
        "stream_velocity": "38.89",
        "stencil_size": "1",
        "point_cloud_size": "500000",
    },
    verbose=True,
)

Refer to the workflows directory for detailed instructions on executing individual reference workflows and samples. These are primarily packaged as Jupyter notebooks where possible, to provide for inline documentation and visualization of expected results.

Contributing to PhysicsNeMo

PhysicsNeMo is an open source collaboration and its success is rooted in community contribution to further the field of Physics-ML. Thank you for contributing to the project so others can build on top of your contribution.

For guidance on contributing to PhysicsNeMo, refer to the contributing guidelines.

Cite PhysicsNeMo

If PhysicsNeMo helped your research and you would like to cite it, refer to the guidelines.

Communication

• Github Discussions: Discuss new architectures, implementations, and Physics-ML research.
• GitHub Issues: Bug reports, feature requests, and install issues.
• PhysicsNeMo Forum: The PhysicsNeMo Forum hosts an audience of new to moderate-level users and developers for general chat, online discussions, and collaboration.

Feedback

Want to suggest some improvements to PhysicsNeMo? Use our feedback form.

License

PhysicsNeMo is provided under the Apache License 2.0, see LICENSE.txt for full license text.