Marcus Haywood-Alexander
Mr Marcus Haywood-AlexanderContact DataAddressETH Zürich Name variantsMarcus Haywood-Alexander | |
Organisations |
Staff of Professorship for Structural Mechanics and Monitoring http://www.chatzi.ibk.ethz.ch/ |
Research Field | Physics-Enhanced Machine Learning for Monitoring Systems Data-driven approaches offer a method to overcome the challenges faced by the complexity of modern structures and systems, provided enough data. However, such data-driven models suffer from being restricted to the domain of the instance in which the data was collected; i.e. they lack generalisability. The fusion of physics with machine learning technologies allows for further progression by; improving generalisability, reducing the amount of data required, and have potential for improving public trust. In the context of Structural Health Monitoring, informed machine learning models will have a significant impact by reducing the data required to adequately encompass the environmental and operational envelope, reducing costs and improving true-detection rates. |
Publications in | Research Collection – Publication platform of ETH Zurich ETH Library Search Portal (advanced search for name) |