The problem of finding the details of a sample without going physically close to it is very challenging. One of the techniques is to shine light onto the sample and collect the light reflected by a sample from afar. However, converting these far-away measurements to the actual details of the sample is a mathematically challenging inverse problem. Both ‘traditional’ physics-based optimization approaches and modern deep learning based approaches are found to be lacking.
The physicists from the nanoRIP project join forces with the artificial intelligence scientists from the *NanoAI project to find new solutions at the junction of these fields. They have recently published an article in IEEE transcations of computational imaging in which they propose powerful mechanisms to integrate techniques from both the fields. The results indeed show a significant improvements over the conventional approaches, see Figure below.
 Z. Liu, M. Roy, D. K. Prasad and K. Agarwal, "Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering," in IEEE Transactions on Computational Imaging, doi: 10.1109/TCI.2022.3158865. Preprint at https://arxiv.org/abs/2111.09109. Early access link: https://ieeexplore.ieee.org/document/9735387