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Efficient quadratures for high-dimensional Bayesian data assimilation
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DOI：10.1016/j.jcp.2024.112945.png)
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Bayesian update is a common strategy used to combine (uncertain) model predictions and (noisy) observational data. A computational bottleneck in this data assimilation technique is the evaluation of high -dimensional quadratures involving multivariate probability density functions (PDFs) of system states. We explore designed quadratures as a means to reduce the computational cost of Bayesian update of multivariate joint PDFs. A series of numerical experiments demonstrate that our method outperforms stochastic collocation on sparse grids, a popular technique used to perform high -dimensional integration in the context of uncertainty quantification, in terms of both accuracy and computational efficiency.
Keyword:
Data assimilation
Designed quadrature
Joint probabilistic density function
期刊
IF:
3.8
论文数:
1.6W
被引数:
7.4W
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