Niching importance sampling yields a robust probability-of-failure estimator that avoids degeneracy on multi-modal performance functions by integrating evolutionary niching with importance sampling.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
KL-DNN uses low-rank SVD and nested Karhunen-Loeve expansions to enable scalable operator learning on large 3D GCS simulations, achieving 0.04% relative pressure error and two-order speedup over DeepONet.
An eigenvalue-based small-sample approximation to MCMC reduces required paths from up to 1,000,000 to as few as 10 while producing comparable steady-state distributions by Wasserstein distance and lower variance.
citing papers explorer
-
Niching Importance Sampling for Multi-modal Rare-event Simulation
Niching importance sampling yields a robust probability-of-failure estimator that avoids degeneracy on multi-modal performance functions by integrating evolutionary niching with importance sampling.