Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

In three linesAdaptive sequential sampling method for polynomial chaos expansion surrogate models, generalized for multiple quantities of interest. The approach balances input space exploration with exploitation of aggregated variance across outputs, improving surrogate accuracy and stability compared to Latin Hypercube Sampling.

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