Structured Machine Learning Tools for Modelling Characteristics of Guided Waves. - Marcus Haywood-Alexander; Nikolaos Dervilis; Keith Worden; Elizabeth J. Cross; Robin S. Mills; Timothy J. Rogers 003 (CoRR, 2021)

Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets. - S. C. Bee; Evangelos Papatheou; Marcus Haywood-Alexander; Robin S. Mills; Lawrence A. Bull; Keith Worden; Nikolaos Dervilis (CoRR, 2023)

Full-scale modal testing of a Hawk T1A aircraft for benchmarking vibration-based methods. - Marcus Haywood-Alexander; Robin S. Mills; Max D. Champneys; Matthew R. Jones; Matthew S. Bonney; David J. Wagg; Timothy J. Rogers 003 (CoRR, 2023)

Discussing the Spectra of Physics-Enhanced Machine Learning via a Survey on Structural Mechanics Applications. - Marcus Haywood-Alexander; Wei Liu; Kiran Bacsa; Zhilu Lai; Eleni N. Chatzi (CoRR, 2023)

A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves. - Marcus Haywood-Alexander; Nikolaos Dervilis; Keith Worden; Robin S. Mills; Purim Ladpli; Timothy J. Rogers 003 (Sensors, 2023)

Machine Learning and statistical classification of CRISPR-Cas12a diagnostic assays. - Nathan Khosla; Jake M. Lesinski; Marcus Haywood-Alexander; Andrew J. deMello; Daniel A. Richards (CoRR, 2025)

Response estimation and system identification of dynamical systems via physics-informed neural networks. - Marcus Haywood-Alexander; Giacomo Arcieri; Antonios Kamariotis; Eleni N. Chatzi (Adv. Model. Simul. Eng. Sci., 2025)