Liam Comerford

Past Research Group members

Liam received his Ph.D. degree in 2015 from University of Liverpool, UK, under the co-supervision of Prof Kougioumtzoglou and Prof. Beer.

Dissertation: Artificial neural network approaches and compressive sensing techniques for stochastic process estimation and simulation subject to incomplete data

Publications

An artificial neural network approach for stochastic process power spectrum estimation subject to missing data

Liam Comerford

Ioannis A. Kougioumtzoglou

Michael Beer

On quantifying the uncertainty of stochastic process power spectrum estimates subject to missing data

Liam Comerford

Ioannis A. Kougioumtzoglou

Michael Beer

Compressive sensing based stochastic process power spectrum estimation subject to missing data

Liam Comerford

Ioannis A. Kougioumtzoglou

Michael Beer

Compressive sensing with an adaptive wavelet basis for structural system response and reliability analysis under missing data

Liam Comerford

Héctor A. Jensen

M. Mayorga

Michael Beer

Ioannis A. Kougioumtzoglou

Incomplete data based parameter identification of nonlinear and time-variant oscillators with fractional derivative elements

Ioannis A. Kougioumtzoglou

Ketson R. M. dos Santos

Liam Comerford

Uncertainty quantification of power spectrum and spectral moments estimates subject to missing data

Yuanjin Zhang

Liam Comerford

Ioannis A. Kougioumtzoglou

Edoardo Patelli

Michael Beer

Lp-norm minimization for stochastic process power spectrum estimation subject to incomplete data

Yuanjin Zhang

Liam Comerford

Ioannis A. Kougioumtzoglou

Michael Beer

Utilising database-driven interactive software to enhance independent home-study in a flipped classroom setting: going beyond visualising engineering concepts to ensuring formative assessment

Liam Comerford

Adam Mannis

Marco DeAngelis

Ioannis A. Kougioumtzoglou

Michael Beer

Liam Comerford

An artificial neural network approach for stochastic process power spectrum estimation subject to missing data

On quantifying the uncertainty of stochastic process power spectrum estimates subject to missing data

Compressive sensing based stochastic process power spectrum estimation subject to missing data

Compressive sensing with an adaptive wavelet basis for structural system response and reliability analysis under missing data

Incomplete data based parameter identification of nonlinear and time-variant oscillators with fractional derivative elements

Uncertainty quantification of power spectrum and spectral moments estimates subject to missing data

Lp-norm minimization for stochastic process power spectrum estimation subject to incomplete data

Utilising database-driven interactive software to enhance independent home-study in a flipped classroom setting: going beyond visualising engineering concepts to ensuring formative assessment

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