Hands On material for the PhD course "Physics Informed Neural Network" AA2025/26

Prerequisites: how to install PhysicsNemo-SYM

Using Apptainer/Singularity from Docker

NVIDIA offers PhysicsNemo-SYM in a pre-defined container in their NVIDIA Container Registry:

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

This means that, if you have apptainer installed (on how to install Apptainer, see their guide here).

The physicsnemo2506.def here can be built

apptainer build physicsnemo2506_container.sif physicsnemo2506.def

And it can either be run interactively

apptainer run physicsnemo2506_container.sif

or added as a kernel to Jupyter in your local space; to do so, we need to create the folder

mkdir -p ~/.local/share/jupyter/kernels/physicsnemo2506

and then create the kernel.json file (e.g., via vim or nano)

{
    "display_name": "PhysicsNemo 2506",
    "argv": [
        "/usr/bin/apptainer",
        "run",
        "--nv",
        "--bind",
        "/home",
        "/path/to/physicsnemo2506_container.sif",
        "python3",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
    ],
    "language": "python",
    "metadata": {
        "debugger": true
    },
    "env": {
        "LD_LIBRARY_PATH": ":/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/.singularity.d/libs:/usr/local/lib/python3.12/dist-packages/nvidia/cudnn"
    }
}

Notice that we added in the env section the LD_LIBRARY_PATH env var, adding to the original one the path to cudnn as /usr/local/lib/python3.12/dist-packages/nvidia/cudnn.

Using Conda

Lectures

The course will be divided in frontal lectures and hands on lectures; this repository contains the code for the hands on sessions.