Deep Learning-Assisted Optimal Sensor Placement in Ultrasound NDT

In this work we employ model-based deep learning to optimally select the sensing locations of single channel synthetic aperture measurements in ultrasound nondestructive testing. We use the Fisher in formation as an optimization target to obtain task-agnostic selection matrices. We then link this result to prior findings on the behavior of the Fisher information matrix.