darcy_fno

Fourier Neural Operater for Darcy Flow

This example demonstrates how to set up a data-driven model for a 2D Darcy flow using the Fourier Neural Operator (FNO) architecture inside of Modulus. Training progress can be tracked through MLFlow. This example runs on a single GPU, go to the darcy_nested_fno example for exploring a multi-GPU training.

Getting Started

Prerequisites

Install the required dependencies by running below:

pip install -r requirements.txt

To train the model, run

python train_fno_darcy.py

training data will be generated on the fly.

Progress can be monitored using MLFlow. Open a new terminal and navigate to the training directory, then run:

mlflow ui -p 2458

View progress in a browser at http://127.0.0.1:2458

If training on a remote machine, set up a ssh tunnel to the server with LocalForward 8080 your_remote_machine_addr:8080. ssh to the server via the specified port, in this case 8080, navigate to the training directory and launch mlflow server

mlflow server --host 0.0.0.0 --port 8080

On your local machine, open a browser and connect to localhost:8080.

Additional Information

References

• Fourier Neural Operator for Parametric Partial Differential Equations