A2.4 - Deep Learning-Assisted Optimal Sensor Placement in Ultrasound NDT

Abstract

In this work we employ model-based deep learning to optimally select the sensing locations of singlechannel synthetic aperture measurements in ultrasound nondestructive testing. We use the Fisher information 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.