Sparse Bayesian Learning for Complex-Valued Rational Approximations. (arXiv:2206.02523v2 [stat.ML] UPDATED)

Surrogate models are used to alleviate the computational burden in
engineering tasks, which require the repeated evaluation of computationally
demanding models of physical systems, such as the efficient propagation of
uncertainties. For models that show a strongly non-linear dependence on their
input parameters, standard surrogate techniques, such as polynomial chaos
expansion, are not sufficient to obtain an accurate representation of the
original model response. Through applying a rational approximation instead, the
approximation error can be efficiently reduced for models whose …

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