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An abstracted framework for training AI surrogates of physical systems using physics-based symbolic loss functions

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Modulus Symbolic (Beta)

Project Status: Active – The project has reached a stable, usable state and is being actively developed. GitHub Code style: black

Modulus Symbolic (Modulus Sym) provides an abstraction layer for using PDE-based symbolic loss functions. Additional information can be found in the Modulus documentation. Users of Modulus versions older than 23.05 can refer to the migration guide for updating to the latest version.

Modulus Packages

Installation

PyPi

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

pip install nvidia-modulus.sym

Container

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

docker pull nvcr.io/nvidia/modulus/modulus:23.05

From Source

Package

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

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

pip install --upgrade pip
pip install .

Container

To build release image, you will need to do the below preliminary steps:

Clone this repo, and download the Optix SDK from https://developer.nvidia.com/designworks/optix/downloads/legacy.

git clone https://github.com/NVIDIA/modulus-sym.git
cd modulus-sym/ && mkdir deps

Currently Modulus supports v7.0. Place the Optix file in the deps directory and make it executable. Also clone the pysdf library in the deps folder (NVIDIA Internal)

chmod +x deps/NVIDIA-OptiX-SDK-7.0.0-linux64.sh 
git clone <internal pysdf repo>

Then to build the image, insert next tag and run below:

docker build -t modulus-sym:deploy -f Dockerfile --target with-pysdf .

Alternatively, if you want to skip pysdf installation, you can run the following:

docker build -t modulus-sym:deploy -f Dockerfile --target no-pysdf .

Windows Container

For building windows just use

docker build -t modulus  - <  Windows.Dockerfile

Additional notes

New modification suppose to work with pip to (you'll just need to install two additional libralies)

pip install trimesh
pip install mesh_to_sdf

Tesselation will work because instead of using pysdf I used mesh_to_sdf (I have not yet implemented sdf derivatives yet, so some functions will not work on windows (It's only a few functions for visualization differences with real data (as far as I know)))

Contributing

For guidance on making a contribution to Modulus, see the contributing guidelines.

Communication

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

License

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

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An abstracted framework for training AI surrogates of physical systems using physics-based symbolic loss functions

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