Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Lykkegaard, Mikkel B."'
In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various multivariate proposal distributions, and compare them with single-site
Externí odkaz:
http://arxiv.org/abs/2405.00397
Autor:
Seelinger, Linus, Reinarz, Anne, Lykkegaard, Mikkel B., Akers, Robert, Alghamdi, Amal M. A., Aristoff, David, Bangerth, Wolfgang, Bénézech, Jean, Diez, Matteo, Frey, Kurt, Jakeman, John D., Jørgensen, Jakob S., Kim, Ki-Tae, Kent, Benjamin M., Martinelli, Massimiliano, Parno, Matthew, Pellegrini, Riccardo, Petra, Noemi, Riis, Nicolai A. B., Rosenfeld, Katherine, Serani, Andrea, Tamellini, Lorenzo, Villa, Umberto, Dodwell, Tim J., Scheichl, Robert
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 this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-l
Externí odkaz:
http://arxiv.org/abs/2402.13768
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 distribution. Broadly, the method rewrites the Multilevel MCMC approach
Externí odkaz:
http://arxiv.org/abs/2202.03876
Autor:
Lykkegaard, Mikkel B, Dodwell, Tim J
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 borrows ideas from Bayesian Optimisation and goal-oriented error estimatio
Externí odkaz:
http://arxiv.org/abs/2111.07670
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 Equati
Externí odkaz:
http://arxiv.org/abs/2012.05668
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 M
Externí odkaz:
http://arxiv.org/abs/2007.00400
Autor:
Lykkegaard, Mikkel B., Dodwell, Tim J.
Publikováno v:
In Advances in Water Resources June 2022 164
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 September 2021 383
Akademický článek
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