61 citations · 62 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
A spectrum of physics-informed Gaussian processes for regression in engineering
Elizabeth J Cross, Timothy J Rogers, Daniel J Pitchforth +2
Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven…
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…