KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.
Reliability analysis of slope stability under seismic condition during a given exposure time.Landslides, 15 (11):2303–2313
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Surrogate-Guided Adaptive Importance Sampling for Failure Probability Estimation
KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.