Mikkel B. Lykkegaard
University of Exeter
Curse of dimensionalityMarkov chain Monte CarloAlgorithmProbability distributionPrecomputationRange (mathematics)Hierarchy (mathematics)Uncertainty quantificationComputer scienceProbabilistic logicArtificial neural networkPosterior probabilityStochastic processPartial differential equation
Publications 2
#2Timothy DodwellH-Index: 13
Last. David MoxeyH-Index: 15
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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 algorithmic approach which fuses Markov Chain Monte Carlo (MCMC) and Machine Learning methods to accelerate uncertainty quantification for groundwater flow models. We formulate the governing mathematical model as a Bayesian inverse problem, considering model parameters as a random process with an underlying probability...
2 CitationsSource
#1Mikkel B. Lykkegaard (University of Exeter)H-Index: 1
#2Grigorios Mingas (The Turing Institute)H-Index: 7
Last. Timothy Dodwell (University of Exeter)H-Index: 13
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Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications. Multilevel Delayed Acceptance (MLDA) with an Adaptive Error Model (AEM) is a novel approach, which alleviates this problem by exploiting a hierarchy of models, with increasing comp...
1 Citations