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Efficient importance sampling for stochastic simulators using adaptive mixture density networks
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DOI：10.1016/j.strusafe.2026.102709.png)
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Abstract
En 中文
• Novel MDN-based importance sampling for stochastic simulator reliability analysis. • CRPS loss training optimizes conditional failure probability estimation accuracy. • Ensemble strategy with pruning reduces surrogate prediction variability. • Substantial fewer samples than IS baselines for equivalent estimation accuracy. • Effective for 12-dim structural reliability under stochastic seismic excitation.
Keywords:
Importance Sampling
Mixture Density Networks
Stochastic Simulators
Reliability Analysis
Conditional Failure Probability
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