Abstract
The present work revisits the computational performance of non-intrusive Monte Carlo versus intrusive Galerkin methods for large-scale stochastic systems in the framework of high performance computing environments. The purpose of this work is to perform an assessment of the range of the relative superiority of these approaches with regard to a variety of stochastic parameters. In both approaches, the solution of the resulting algebraic equations is performed with a combination of primal and dual domain decomposition methods implementing specifically tailored preconditioners.
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Acknowledgements
This work has been supported by the European Research Council Advanced Grant MASTER – Mastering the computational challenges in numerical modeling and optimum design of CNT reinforced composites (ERC-2011-ADG-20110209).
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Stavroulakis, G., Giovanis, D.G., Papadrakakis, M., Papadopoulos, V. (2014). Monte Carlo Simulation vs. Polynomial Chaos in Structural Analysis: A Numerical Performance Study. In: Papadrakakis, M., Stefanou, G. (eds) Multiscale Modeling and Uncertainty Quantification of Materials and Structures. Springer, Cham. https://doi.org/10.1007/978-3-319-06331-7_14
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DOI: https://doi.org/10.1007/978-3-319-06331-7_14
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