Mr Mikkel Bue Lykkegaard
Postgraduate Researcher (WISE CDT)
Mikkel’s research is concerned with Bayesian inversion for hydrogeology – specifically using cutting-edge Markov Chain Monte Carlo (MCMC) techniques for groundwater flow parameter estimation and uncertainty quantification. He is particularly interested in the use of surrogate models in multi-level MCMC model hierarchies, and is currently exploring cutting-edge Machine Learning techniques for ultra-fast approximation of model response.His work has applications in both groundwater abstraction and remediation – improved estimates of groundwater flow patterns can improve decision support systems, allowing groundwater abstraction companies to make better sustainable yield estimates, and remediation companies to design taylor-made remediation campaigns.
- Environmental (geo-)hydrology and hydroinformatics
- Uncertainty quantification and model sensitivity analysis
- Distributed environmental models and surrogate models
- Environmental fate and risk assessment of pollutants