The Ensemble Kalman Filter for Rare Event Estimation

Fabian Wagner, I. Papaioannou, E. Ullmann

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We present a novel sampling-based method for estimating probabilities of rare or failure events. Our approach is founded on the ensemble Kalman filter (EnKF) for inverse problems. Therefore, we reformulate the rare event problem as an inverse problem and apply the EnKF to generate failure samples. To estimate the probability of failure, we use the final EnKF samples to fit a distribution model and apply importance sampling with respect to the fitted distribution. This leads to an unbiased estimator if the density of the fitted distribution admits positive values within the whole failure domain. To handle multimodal failure domains, we localize the covariance matrices in the EnKF update step around each particle and fit a mixture distribution model in the importance sampling step. For affine linear limit-state functions, we investigate the continuous time limit and large time properties of the EnKF update. We prove that the mean of the particles converges to a convex combination of the most likely failure point and the mean of the optimal importance sampling density if the EnKF is applied without noise. We provide numerical experiments to compare the performance of the EnKF with sequential importance sampling.

Original languageEnglish
Pages (from-to)317-349
Number of pages33
JournalSIAM-ASA Journal on Uncertainty Quantification
Volume10
Issue number1
DOIs
StatePublished - 2022

Keywords

  • ensemble Kalman filter
  • importance sampling
  • inverse problems
  • reliability analysis

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