My research interests revolve around using machine learning methods, specifically Bayesian methods and Gaussian Processes, to add value in an engineering context. Of particular interest is the role of these methods in Structural Health Monitoring (SHM) and nonlinear system identification.
My research is currently focused on structural monitoring and nonlinear system identification on offshore platforms working with the Structural Monitoring Systems department at Ramboll. Our research falls into two main themes, the first being to understand the physical mechanisms driving nonlinear behaviour on the structures of interest; the second being the combination of engineering insight with state of the art machine learning techniques to better model physical processes.
A particular interest is in the problem of wave loading on offshore structures, where the current physical models are not sufficient to fully describe the complex nonlinear loading that the structures experience. Since understanding the loading experienced by a structure is key to understanding possible damage and the dynamic behaviour, it is hoped that a model of wave loading with better predictive accuracy will be able to facilitate better analysis of the dynamic behaviour of the structure.
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