How to run a use case

Each use case comes with their own tutorial on how to run it. Before running them, however, you should set up a Python virtual environment. Refer to the getting started section for more information on how to do this.

After installing and activating the virtual environment, you will want to install the use-case specific dependencies, if applicable. This can be done by first cd-ing into the use-case directory and then installing the requirements, as follows

cd use-cases/<name-of-use-case>
pip install -r requirements.txt

Alternatively, you can use the use-case Docker image, if available. After setting everything up, you can now run the use case as specified in the use case’s tutorial.

Fast particle detector simulation | CERN

The first interTwin use case integrated with itwinai framework is the DT for fast particle detector simulation. 3D Generative Adversarial Network (3DGAN) for generation of images of calorimeter depositions. This project is based on the prototype 3DGAN model developed at CERN and is implemented on PyTorch Lightning framework.

3DGAN
- itwinai x 3DGAN
- interLink x 3DGAN

MNIST dataset

MNIST image classification is used to provide an example on how to define an end-to-end digital twin workflow with the itwinai software.

MNIST
- Torch Lightning
- PyTorch

Tropical Cyclones Detection | CMCC

You can find more information on the itwinai integration of the Tropical Cyclones (TCs) Detection model, developed by CMCC, in the Tropical Cyclones Detection documentation.

Noise Simulation for Gravitational Waves Detector (Virgo) | INFN

You can find more information on the Virgo use-case integration with the itwinai framework in the Virgo documentation.

Drought Early Warning in the Alps | EURAC

You can find more information on the EURAC use-case integration with the itwinai in the EURAC documentation.