61 citations · 105 across the 4 of their papers we have counts for
5 papers
From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?
Daisy R. Bradley, Nathan A. Hinchliffe, Daniel J. Pitchforth +2
Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost. Physics-informed machine learnin…
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…
Grey-box models for wave loading prediction
Daniel J Pitchforth, Timothy J Rogers, Ulf T Tygesen +1
The quantification of wave loading on offshore structures and components is a crucial element in the assessment of their useful remaining life. In many applications the well-known…