activity
20242026
collaborators

8 papers

cs.LG2026

Transfer learning via interpolating structures

T. A. Dardeno, A. J. Hughes, L. A. Bull +3

Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a…

cs.LG2026

On Additive Gaussian Processes for Wind Farm Power Prediction

Simon M. Brealy, Lawrence A. Bull, Daniel S. Brennan +4

Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring t…

cs.LG2025

Inter-turbine Modelling of Wind-Farm Power using Multi-task Learning

Simon M. Brealy, Lawrence A. Bull, Pauline Beltrando +3

Because of the global need to increase power production from renewable energy resources, developments in the online monitoring of the associated infrastructure is of interest to re…

cs.LG2025

Regularising NARX models with multi-task learning

Sarah Bee, Lawrence Bull, Nikolaos Dervilis +1

A Nonlinear Auto-Regressive with eXogenous inputs (NARX) model can be used to describe time-varying processes; where the output depends on both previous outputs and current/previou…

cs.LG2024

When does a bridge become an aeroplane?

Tina A. Dardeno, Lawrence A. Bull, Nikolaos Dervilis +1

Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a…

stat.ML2024

Meta-models for transfer learning in source localisation

Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross +2

In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent…