Improved ultrasonic imaging for NDE applications

传统上，无损评估中的超声成像依赖于关于波传播的基本假设，这些假设大大简化了问题并能够快速生成图像，但这些假设往往限制了我们可靠地提取所有可用信息的能力。该项目旨在开发新的方法，将更先进的物理模型纳入成像过程，以提高分辨率和精度。这可能包括全波形反转(FWI)方法以及机器学习(ML)技术。

Traditionally, ultrasonic imaging in NDE (Non-Destructive Evaluation) has relied on rudimentary assumptions about wave propagation which greatly simplify the problem and enable images to be generated quickly.However, these assumptions often limit our capability for reliably extracting all the information available. This project aims to develop new methods to incorporate more advanced physical models into the imaging process, for improved resolution and accuracy. This could include full waveform inversion (FWI) methods as well as Machine Learning (ML) techniques.