PhysicsNeMo Symbolic

📝 NVIDIA Modulus has been renamed to NVIDIA PhysicsNeMo

Getting Started | Install guide | Contributing Guidelines | Resources | PhysicsNeMo Migration Guide | Communication

What is PhysicsNeMo Symbolic?

PhysicsNeMo Symbolic (PhysicsNeMo Sym) repository is part of PhysicsNeMo SDK and it provides algorithms and utilities to be used with PhysicsNeMo core, to explicitly physics inform the model training. This includes utilities for explicitly integrating symbolic PDEs, domain sampling and computing PDE-based residuals using various gradient computing schemes.

It also provides higher level abstraction to compose a training loop from specification of the geometry, PDEs and constraints like boundary conditions using simple symbolic APIs. Please refer to the Lid Driven cavity that illustrates the concept.

Additional information can be found in the PhysicsNeMo documentation.

Please refer to the PhysicsNeMo SDK to learn more about the full stack.

Hello world

You can run below example to start using the geometry module from PhysicsNeMo-Sym as shown below:

>>> import numpy as np
>>> from physicsnemo.sym.geometry.primitives_3d import Box
>>> from physicsnemo.sym.utils.io.vtk import var_to_polyvtk
>>> nr_points = 100000
>>> box = Box(point_1=(-1, -1, -1), point_2=(1, 1, 1))
>>> s = box.sample_boundary(nr_points=nr_points)
>>> var_to_polyvtk(s, "boundary")
>>> print("Surface Area: {:.3f}".format(np.sum(s["area"])))
Surface Area: 24.000

To use the PDE module from PhysicsNeMo-Sym, you can run the below example:

>>> from physicsnemo.sym.eq.pdes.navier_stokes import NavierStokes
>>> ns = NavierStokes(nu=0.01, rho=1, dim=2)
>>> ns.pprint()
continuity: u__x + v__y
momentum_x: u*u__x + v*u__y + p__x + u__t - 0.01*u__x__x - 0.01*u__y__y
momentum_y: u*v__x + v*v__y + p__y + v__t - 0.01*v__x__x - 0.01*v__y__y

To use the computational graph builder from PhysicsNeMo Sym:

>>> import torch
>>> from sympy import Symbol
>>> from physicsnemo.sym.graph import Graph
>>> from physicsnemo.sym.node import Node
>>> from physicsnemo.sym.key import Key
>>> from physicsnemo.sym.eq.pdes.diffusion import Diffusion
>>> from physicsnemo.sym.models.fully_connected import FullyConnectedArch
>>> net = FullyConnectedArch(input_keys=[Key("x")], output_keys=[Key("u")], nr_layers=3, layer_size=32)
>>> diff = Diffusion(T="u", time=False, dim=1, D=0.1, Q=1.0)
>>> nodes = [net.make_node(name="net")] + diffusion.make_nodes()
>>> graph = Graph(nodes, [Key("x")], [Key("diffusion_u")])
>>> graph.forward({"x": torch.tensor([1.0, 2.0]).requires_grad_(True).reshape(-1, 1)})
{'diffusion_u': tensor([[-0.9956],
        [-1.0161]], grad_fn=<SubBackward0>)}

Please refer Introductory Example for usage of the physics utils in custom training loops and Lid Driven cavity for an end-to-end PINN workflow.

Users of PhysicsNeMo versions older than 23.05 can refer to the migration guide for updating to the latest version.

Getting started

The following resources will help you in learning how to use PhysicsNeMo. The best way is to start with a reference sample and then update it for your own use case.

• Using PhysicsNeMo Sym with your PyTorch model
• Using PhysicsNeMo Sym to construct computational graph
• Reference Samples
• User guide Documentation

Resources

• Getting started Webinar
• AI4Science PhysicsNeMo Bootcamp

Installation

PyPi

The recommended method for installing the latest version of PhysicsNeMo Symbolic is using PyPi:

pip install "Cython"
pip install nvidia-physicsnemo.sym --no-build-isolation

Note, the above method only works for x86/amd64 based architectures. For installing PhysicsNeMo Sym on Arm based systems using pip, Install VTK from source as shown here and then install PhysicsNeMo-Sym and other dependencies.

pip install nvidia-physicsnemo.sym --no-deps
pip install "hydra-core>=1.2.0" "termcolor>=2.1.1" "chaospy>=4.3.7" "Cython==0.29.28" \
    "numpy-stl==2.16.3" "opencv-python==4.5.5.64" "scikit-learn==1.0.2" \
    "symengine>=0.10.0" "sympy==1.12" "timm>=1.0.3" "torch-optimizer==0.3.0" \
    "transforms3d==0.3.1" "typing==3.7.4.3" "pillow==10.0.1" "notebook==6.4.12" \
    "mistune==2.0.3" "pint==0.19.2" "tensorboard>=2.8.0"

Container

The recommended PhysicsNeMo docker image can be pulled from the NVIDIA Container Registry:

docker pull nvcr.io/nvidia/physicsnemo/physicsnemo:<tag>

From Source

Package

For a local build of the PhysicsNeMo Symbolic Python package from source use:

git clone git@github.com:NVIDIA/physicsnemo-sym.git && cd physicsnemo-sym

pip install --upgrade pip
pip install .

Source Container

To build release image insert next tag and run below:

docker build -t physicsnemo-sym:deploy \
    --build-arg TARGETPLATFORM=linux/amd64 --target deploy -f Dockerfile .

Currently only linux/amd64 and linux/arm64 platforms are supported.

PhysicsNeMo Migration Guide

NVIDIA Modulus has been renamed to NVIDIA PhysicsNeMo. For migration:

• Use pip install nvidia-physicsnemo.sym rather than pip install nvidia-modulus.sym for PyPi wheels.
• Use nvcr.io/nvidia/physicsnemo/physicsnemo:<tag> rather than nvcr.io/nvidia/modulus/modulus:<tag> for Docker containers.
• Replace nvidia-modulus.sym by nvidia-physicsnemo.sym in your pip requirements files (requirements.txt, setup.py, setup.cfg, pyproject.toml, etc.)
• In your code, change the import statements from import modulus.sym to import physicsnemo.sym

The old PyPi registry and the NGC container registry will be deprecated soon and will not receive any bug fixes/updates. The old checkpoints will remain compatible with these updates.

More details to follow soon.

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, please refer to the contributing guidelines.

Cite PhysicsNeMo

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

Communication

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

Feedback

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

License

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