31 citations · 40 across the 8 of their papers we have counts for
8 papers
Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting
Mikkel Bue Lykkegaard, Svend Vendelbo Nielsen, Akanksha Upadhyay +2
Efficient time series forecasting is essential for smart energy systems, enabling accurate predictions of energy demand, renewable resource availability, and grid stability. Howeve…
A Performance Analysis of Task Scheduling for UQ Workflows on HPC Systems
Chung Ming Loi, Anne Reinarz, Mikkel Lykkegaard +3
Uncertainty Quantification (UQ) workloads are becoming increasingly common in science and engineering. They involve the submission of thousands or even millions of similar tasks wi…
Democratizing Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard +22
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In thi…
Lowering the Entry Bar to HPC-Scale Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Jean Benezech +3
Treating uncertainties in models is essential in many fields of science and engineering. Uncertainty quantification (UQ) on complex and computationally costly numerical models nece…
Multilevel Delayed Acceptance MCMC
Mikkel B. Lykkegaard, Tim J. Dodwell, Colin Fox +2
We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target d…
Where to Drill Next? A Dual-Weighted Approach to Adaptive Optimal Design of Groundwater Surveys
Mikkel B Lykkegaard, Tim J Dodwell
We present a novel approach to adaptive optimal design of groundwater surveys - a methodology for choosing the location of the next monitoring well. Our dual-weighted approach borr…
Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3
Mikkel B. Lykkegaard, Grigorios Mingas, Robert Scheichl +2
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance…
Accelerating Uncertainty Quantification of Groundwater Flow Modelling Using a Deep Neural Network Proxy
Mikkel B. Lykkegaard, Tim J. Dodwell, David Moxey
Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management. This paper presents a novel…