%AHui Xu, Mircea%D2023%I
%K
%MOSTI ID: 10465379
%PMedium: X
%TFinite dimensional surrogates for extreme events
%XNumerical solutions of stochastic problems require the representation of random
functions in their definitions by finite dimensional (FD) models, i.e., deterministic functions of time and
finite sets of random variables. It is common to represent the coefficients of these FD surrogates by
polynomial chaos (PC) models. We propose a novel model, referred to as the polynomial chaos
translation (PCT) model, which matches exactly the marginal distributions of the FD coefficients and
approximately their dependence. PC- and PCT- based FD models are constructed for a set of test cases
and a wind pressure time series recorded at the boundary layer wind tunnel facility at the University of
Florida. The PCT-based models capture the joint distributions of the FD coefficients and the extremes of
target times series accurately while PC-based FD models do not have this capability.
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