A new RBTO method uses large deviation theory for closed-form rare-event probability estimates and SGD updates to optimize structures while meeting explicit reliability targets on beam benchmarks.
Courier Corporation
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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.
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Reliability-based Topology Optimization using Large Deviation Theory
A new RBTO method uses large deviation theory for closed-form rare-event probability estimates and SGD updates to optimize structures while meeting explicit reliability targets on beam benchmarks.
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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.