Computational Electromagnetics
Running heavy algorithms like MEEP FDTD to simulate electromagnetic structure may be time and ressource consuming. Using CWA lets one optimize time and efficiency for large simulations campaigns. Send simulations parameters, run them and retrieve results.
Quantitative Magnetic Resonance Imaging (quantitative MRI, qMRI, MRI, SoQut Imaging 2017-2021)
Quantitative Magnetic Resonance Imaging (quantitative MRI, qMRI, MRI) involves processing DICOM files. These files are not designed for intensive data processing and parallel computation. Using CWA, one can overcome this limitation and, paired with a well chosen PACS (Picture, Archiving and Communication System) software, get a complete intensive data processing software suite “on premise” or in the Cloud. This work has been initially done for SoQut Imaging. Finally, why not combining CWA with hMRI-toolbox, qMRLab or PyQMRI?
An example is available on the repository.
Artificial Intelligence Model Training
Training an AI model may be time and ressource consuming. One can chose a adequate HPC, deploy CWA on it to send data, control and monitor processes designed with Keras, PyTorch or TensorFlow. Send training data with consistent parameters, run model training, retrieve resulting model and it’s done.
