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Titel:

High Performance Uncertainty Quantification with Parallelized Multilevel Markov Chain Monte Carlo

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Linus Seelinger; Anne Reinarz; Leonhard Rannabauer; Michael Bader; Peter Bastian; Robert Scheichl
Abstract:
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 UQ algor...     »
Stichworte:
bayesian inverse problems, multilevel methods, tsunami simulation, ADER-DG
Horizon 2020:
grant agreement No. 828947 (project ENERXICO)
Kongress- / Buchtitel:
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21)
Verlag / Institution:
Association for Computing Machinery
Verlagsort:
New York, NY, USA
Jahr:
2021
Print-ISBN:
9781450384421
Serientitel:
SC '21
Reviewed:
ja
Volltext / DOI:
doi:10.1145/3458817.3476150
WWW:
https://doi.org/10.1145/3458817.3476150
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