194 citations · 255 across the 3 of their papers we have counts for
3 papers
Physically-informed change-point kernels for structural dynamics
Daniel James Pitchforth, Matthew Rhys Jones, Samuel John Gibson +1
The relative balance between physics and data within any physics-informed machine learner is an important modelling consideration to ensure that the benefits of both physics and da…
Improving aircraft performance using machine learning: a review
Soledad Le Clainche, Esteban Ferrer, Sam Gibson +3
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (expe…
Physics-informed machine learning for Structural Health Monitoring
Elizabeth J Cross, Samuel J Gibson, Matthew R Jones +3
The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-b…