Multi-task neural networks learn tasks simultaneously to improve individual task performance. There are three mechanisms of multi-task learning (MTL) which are explored here for the context of structural health monitoring (SHM): (i) the natural occurrence of multiple tasks; (ii) using outputs as inputs (both linked to the recent research in population-based SHM (PBSHM)); and, (iii) additional loss functions to provide different insights. Each of these problem settings for MTL is detailed and an example is given.
@article{arxiv.2305.09425,
title = {When is an SHM problem a Multi-Task-Learning problem?},
author = {Sarah Bee and Lawrence Bull and Nikolas Dervilis and Keith Worden},
journal= {arXiv preprint arXiv:2305.09425},
year = {2023}
}