Physics-guided transfer learning for lightweight structural dynamics testing

Machine Learning algorithms applied to a damage detection context can achieve state-of-the-art results in an automated manner. In many structural dynamics testing scenarios however, there is a data scarcity (in particular of labelled data), which can prevent the successful development of such models. This project aims to create a solution to the above challenge by establishing new physics-guided transfer learning frameworks applied to lightweight structural dynamics testing.