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Found 3 papers, 1 papers with code
Sparse Bayesian Learning for Complex-Valued Rational Approximations
For models that require a high polynomial degree or are high-dimensional in terms of their input parameters, this number often exceeds the affordable computational cost.
Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation
Although some existing parametric distribution families are designed to perform efficiently in high dimensions, their applicability within the cross-entropy method is limited to problems with dimension of O(1e2).
Computation
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations.
