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Found 2 papers, 2 papers with code
Magnetic Field Prediction Using Generative Adversarial Networks
By minimizing this statistical distance, a reconstruction loss as well as physical losses, our trained generator has learned to predict the missing field values with a median reconstruction test error of 5. 14%, when a single coherent region of field points is missing, and 5. 86%, when only a few point measurements in space are available and the field measurements around are predicted.
A Physical Model for Microstructural Characterization and Segmentation of 3D Tomography Data
Instead, the material parameters are quantified through their known relation to parameters of our model which is fitted directly to the raw, unsegmented data.
Materials Science Image and Video Processing
