Overview

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 has contributed to the Multilevel Delayed Acceptance (MLDA) sampler to PyMC3 in collaboration with researchers from the ALan Turing Institute, and written an open source gradient-free Delayed Acceptance MCMC framework, tinyDA, which he uses in his own research.

His current research is concerned with adaptive optimal design of groundwater surveys, motivated by the question "where to drill next?, where he is investigating various adjoint methods in conjunction with Monte Carlo uncertainty quantification. He is also working on different parallelisation strategies for Delayed Acceptance MCMC.

Research Interests:

• Environmental (geo-)hydrology and hydroinformatics
• Monte Carlo methods
• Uncertainty quantification for Bayesian inverse problems
• Distributed environmental models
• Environmental fate and risk assessment of pollutants

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Publications

_{Copyright Notice: Any articles made available for download are for personal use only. Any other use requires prior permission of the author and the copyright holder.}

| 2024 | 2022 | 2020 |

2024

• Seelinger L, Reinarz A, Lykkegaard MB, Alghamdi AMA, Aristoff D, Bangerth W, Bénézech J, Diez M, Frey K, Jakeman JD. (2024) Democratizing Uncertainty Quantification. [PDF]

2022

• Lykkegaard MB, Dodwell TJ. (2022) Where to drill next? A dual-weighted approach to adaptive optimal design of groundwater surveys, Advances in Water Resources, volume 164, DOI:10.1016/j.advwatres.2022.104219.

2020

• Lykkegaard MB, Dodwell TJ, Moxey D. (2020) Accelerating Uncertainty Quantification of Groundwater Flow Modelling Using a Deep Neural Network Proxy, DOI:10.48550/arxiv.2007.00400.
• Lykkegaard MB, Mingas G, Scheichl R, Fox C, Dodwell TJ. (2020) Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3, DOI:10.48550/arxiv.2012.05668.

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