# High performance uncertainty quantification with parallelized multilevel Markov chain Monte Carlo

@article{Seelinger2021HighPU, title={High performance uncertainty quantification with parallelized multilevel Markov chain Monte Carlo}, author={Linus Seelinger and Anne Reinarz and Leonhard Rannabauer and Michael Bader and Peter Bastian and Robert Scheichl}, journal={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis}, year={2021} }

Numerical models of complex real-world phenomena often necessitate High Performance Computing (HPC). Uncertainties increase problem dimensionality further and pose even greater challenges. We present a parallelization strategy for multilevel Markov chain Monte Carlo, a state-of-the-art, algorithmically scalable Uncertainty Quantification (UQ) algorithm for Bayesian inverse problems, and a new software framework allowing for large-scale parallelism across forward model evaluations and the UQβ¦Β

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