Devlin, L., Carter, C., Horridge, P., Green, P.L. & Maskell, S. (2024). The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject. IEEE Signal Processing Letters, 10.1109/LSP.2024.3386494.
Echeverria-Rios, D., & Green, P. L. (2024). Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach. Engineering Applications of Artificial Intelligence, 127, 107233. doi:10.1016/j.engappai.2023.107233
Jayasinghe, S., Paoletti, P., Jones, N., & Green, P. L. (2023). Predicting gas pores from photodiode measurements in laser powder bed fusion builds. Progress in Additive Manufacturing, 1-4.
Wu, J., Wen, L., Green, P. L., Li, J., & Maskell, S. (2022). Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference. Statistics and Computing, 32(1), 20.
Green, P. L., Moore, R. E., Jackson, R. J., Li, J., & Maskell, S. (2022).
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels. Mechanical Systems and Signal Processing, 162, 108028.
Jayasinghe, S., Paoletti, P., Sutcliffe, C., Dardis, J., Jones, N., & Green, P.L. (2021).
Carrer, N. L., & Green, P. L. (2020).
A possibilistic interpretation of ensemble forecasts: experiments on the imperfect Lorenz 96 system. Advances in Science and Research, 17, 39-45.
Roberts, J. W., Sutcliffe, C. J., Green, P. L., & Black, K. (2020).
Modelling of metallic particle binders for increased part density in binder jet printed components. Additive Manufacturing, 34, 101244.
Jackson, R. D., Jump, M., & Green, P. L. (2020).
Predicting On-axis Rotorcraft Dynamic Responses Using Machine Learning Techniques. Journal of the American Helicopter Society, 65(3), 1-12.
Le Carrer, N., Ferson, S., & Green, P. L. (2020).
Optimising cargo loading and ship scheduling in tidal areas. European Journal of Operational Research, 280(3), 1082-1094.
Okaro, I. A., Jayasinghe, S., Sutcliffe, C., Black, K., Paoletti, P., & Green, P. L. (2019).
Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Additive Manufacturing, 27, 42-53.
Mendoza-Puchades, M., Green, P. L., & Judge, R. (2018).
Variability in masonry behaviour and modelling under blast and seismic actions. Proceedings of the Institution of Civil Engineers-Structures and Buildings, 171(10), 768-777.
Green, P. L., & Maskell, S. (2017).
Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing, 93, 379-396.
Ahmed, T. M., Green, P. L., & Khalid, H. A. (2017).
Predicting fatigue performance of hot mix asphalt using artificial neural networks. Road Materials and Pavement Design, 18(sup2), 141-154.
Worden, K., & Green, P. L. (2017).
A machine learning approach to nonlinear modal analysis. Mechanical Systems and Signal Processing, 84, 34-53.
Scott, M., Green, P. L., O’Driscoll, D., Worden, K., & Sims, N. D. (2016).
Sensitivity analysis of an Advanced Gas-cooled Reactor control rod model. Nuclear Engineering and Design, 305, 514-523.
Green, P. L., Hendijanizadeh, M., Simeone, L., & Elliott, S. J. (2016).
Probabilistic modelling of a rotational energy harvester. Journal of Intelligent Material Systems and Structures, 27(4), 528-536.
Hill, T. L., Green, P. L., Cammarano, A., & Neild, S. A. (2016).
Fast Bayesian identification of a class of elastic weakly nonlinear systems using backbone curves. Journal of sound and vibration, 360, 156-170.
Green, P. L., & Worden, K. (2015).
Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2051), 20140405.
Green, P. L. (2015).
Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution. Probabilistic Engineering Mechanics, 42, 54-63.
Stevanović, N., Green, P. L., Worden, K., & Kirkegaard, P. H. (2016).
Friction estimation in wind turbine blade bearings. Structural Control and Health Monitoring, 23(1), 103-122.
Green, P. L., Cross, E. J., & Worden, K. (2015).
Bayesian system identification of dynamical systems using highly informative training data. Mechanical systems and signal processing, 56, 109-122.
Green, P. L. (2015).
Bayesian system identification of a nonlinear dynamical system using a novel variant of simulated annealing. Mechanical Systems and Signal Processing, 52, 133-146.
Green, P. L., Worden, K., & Sims, N. D. (2013).
On the identification and modelling of friction in a randomly excited energy harvester. Journal of Sound and Vibration, 332(19), 4696-4708.
Green, P. L., Papatheou, E., & Sims, N. D. (2013).
Energy harvesting from human motion and bridge vibrations: An evaluation of current nonlinear energy harvesting solutions. Journal of Intelligent Material Systems and Structures, 24(12), 1494-1505.
Green, P. L., Worden, K., Atallah, K., & Sims, N. D. (2012).
The effect of Duffing-type non-linearities and Coulomb damping on the response of an energy harvester to random excitations. Journal of Intelligent Material Systems and Structures, 23(18), 2039-2054.
Green, P. L., Worden, K., Atallah, K., & Sims, N. D. (2012).
The benefits of Duffing-type nonlinearities and electrical optimisation of a mono-stable energy harvester under white Gaussian excitations. Journal of Sound and Vibration, 331(20), 4504-4517.