Generatives models approach on TFM data based on variational inference for multi-fidelity data generation

The present work evaluates the translation from a numerical input into a higher fidelity output by transferring experimental style to simulated data. The hybrid and experimental data similarities are measured to validate the newly generated images. Future work aims to use the hybrid data to enhance the data generation to be used for training classification and regression models in view of decision support systems.