Stochastic Galerkin methods can be used to approximate the solution to a differential equation in the presence of uncertainties represented as stochastic inputs or parameters. The strategy is to express the resulting stochastic solution using $$M + 1$$ terms of a polynomial chaos expansion and then derive and solve a deterministic, coupled system of PDEs with standard numerical techniques. One of the critical advantages of this approach is its provable convergence as M increases. The challenge is that the solution to the M system cannot easily reuse an already-existing computer solution to the $$M-1$$ system. We present a promising iterative strategy to address this issue. Numerical estimates of the accuracy and efficiency of the proposed algorithm (bluff-and-fix) demonstrate that it can be more effective than using monolithic methods to solve the whole M + 1 system directly.
CITATION STYLE
Lyman, L., & Iaccarino, G. (2020). A bluff-and-fix algorithm for polynomial chaos methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12143 LNCS, pp. 742–756). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-50436-6_55
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