Selection of Publications from 2008

P. C. Young, P. G. Allen, and J. T. Bruun. A re-evaluation of the Earth’s surface temperature response to radiative forcing. Environmental Research Letters, 16(5):054068, 2021.

P. C. Young and F. Chen. Monitoring and forecasting the COVID-19 epidemic in the UK. Annual Reviews in Control, 51:488–499, 2021

F. Chen, P. C. Young, H. Garnier, Q. Deng, and M. K. Kazimierczuk. Data-driven modeling of wireless power transfer systems with multiple transmitters,. IEEE Transactions on Power Electronics, 35(11):11363–11379, 2020.

P. C. Young and A. Janot. Efficient parameterisation of nonlinear system models: a comment on Noel and Schoukens (2018). International Journal of Control, 93(7):1591–1595, 2018.

P. C. Young. Data-based mechanistic modelling and forecasting globally averaged surface temperature. International Journal of Forecasting, 34:315–334, 2018.

M. Brunot, A. Janot, P. C. Young, and F. Carrillo. An improved instrumental variable method for industrial robot identification. Control Engineering Practice, 74:107–117, 2018.

A. Janot, P. C. Young, and M. Gautier. Identification and control of electro-mechanical systems using state-dependent parameter estimation. International Journal of Control, 90(4):643–660., 2017.

P. C. Young. Data-based mechanistic modeling. In V. P. Singh, editor, Handbook of Applied Hydrology, chapter 33, pages 1–12. McGraw Hill, USA, 2016.

P. C. Young. Refined instrumental variable estimation: Maximum likelihood optimization of a unified Box-Jenkins model. Automatica, 52:35–46, 2015.

H.Garnier and P. C. Young (2014) The advantages of directly identifying continuous-time transfer function models in practical applications. Int. J. Control (in press).

P. C. Young (2013) Hypothetico-inductive data-based mechanistic modeling of hydrological systems. Water Resources Research, 49(2):915–935.

P.C. Young (2012) Data-based mechanistic modelling: natural philosophy revisited? In L.Wang and H.Garnier, editors, System Identification, Environmetric Modelling and Control, Berlin, Springer-Verlag.

K. J. Beven, D.T. Leedal, P.J. Smith, and P.C. Young (2012) Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology. In L.Wang and H.Garnier, editors, System Identification, Environmetric Modelling and Control, pages 341–366, Berlin, Springer-Verlag.

P.C. Young and M.Ratto (2011) Statistical emulation of large linear dynamic models. Technometrics, 53:29–43.

P. C. Young (2011) Data-based mechanistic modeling: natural philosophy revisited? In L. Wang and H. Garnier, editors, System Identification, Environmetric Modelling and Control System Design, Springer-Verlag: London.

K. J. Beven, D. T. Leedal, P. J. Smith, and P. C. Young (2011) Identification and representation of state dependent nonlinearities in flood forecasting using the DBM methodology. In L. Wang and H. Garnier, editors, System Identification, Environmetric Modelling and Control System Design, Springer-Verlag: London.

C. X. Lu, N. W. Rees, and P. C. Young (2011) Simulation model emulation in control system design. In L. Wang, H. Garnier, and A. J. Jakeman, editors, System Identification, Environmetric Modelling and Control System Design, Springer-Verlag: London.

N.McIntyre, P.C. Young, B.Orellana, M.Marshall, B.Reynolds, and H.Wheater. Identification of nonlinearity in rainfall-flow response using data-based mechanistic modeling. Water Resour. Res., 47:W03515, doi:10.1029/2010WR009851, 2011.

P.C. Young. Gauss, Kalman and advances in recursive parameter estimation. Journal of Forecasting (special issue celebrating 50 years of the Kalman Filter), 30:104–146, 2010.

P.C. Young. Real-time updating in flood forecasting and warning. In G.J. Pender and H.Faulkner, editors, Flood Risk Science and Management, pages 163–195, Oxford, UK, 2010. Wiley-Blackwell.

P.C. Young and M.Ratto. A unified approach to environmental systems modeling. Stochastic Environmental Research and Risk Assessment, 23:1037–1057, 2009.

Young, P.C., Garnier, H., and Gilson, M. (2008). Refined instrumental variable identification of continuous-time hybrid Box-Jenkins models. In Garnier, H. and Wang, L., editors, Identification of Continuous-Time Models from Sampled Data, pages 91–131. Springer-Verlag: London.