# When is an SHM problem a Multi-Task-Learning problem?

arXiv: https://arxiv.org/abs/2305.09425 · HTML: https://mlsanity.com/paper/2305.09425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 2 of 2)

- [Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces](https://mlsanity.com/paper/2103.03655/) (2021)
- [Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning](https://mlsanity.com/paper/2204.12404/) (2022)

## Built on by (most cited first, 1 of 1)

- [Multi-Task Equation Discovery](https://mlsanity.com/paper/2509.25400/) (2025)

## Abstract (arXiv)

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.