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Adaptive stratified sampling for structural reliability analysis
https://orcid.org/0000-0001-6603-4914
Abstract An adaptive framework of stratified sampling is established for estimating the probability of failure in structural reliability analysis in combination with variance reduction employed by strata. Among a variety of variance reduction techniques, stratification of Latin hypercube type makes it possible to apply importance sampling and control variates by strata in an adaptive manner without the need to track differences in computing cost across strata. By batching the resulting paralleled tasks, the proposed algorithm adjusts the allocation of computing budget only occasionally yet effectively taking full account of decreasing stratum variances. A variety of structural reliability problems with sparse structures lie within the scope of the proposed framework in the sense that stratification with gradual adjustment of computing budget avoids wasting the resource on possibly many empty strata, while importance sampling and control variates improve Monte Carlo methods on non-empty strata. We provide numerical results to illustrate the wide applicability and effectiveness of the proposed framework.
Highlights An adaptive framework of stratified sampling is proposed for reliability analysis. The budget allocation is updated via a dynamic scheme periodically. Adaptive importance sampling and control variates is employed by strata. The proposed method is effective for various complex limit state functions.
Adaptive stratified sampling for structural reliability analysis
https://orcid.org/0000-0001-6603-4914
Abstract An adaptive framework of stratified sampling is established for estimating the probability of failure in structural reliability analysis in combination with variance reduction employed by strata. Among a variety of variance reduction techniques, stratification of Latin hypercube type makes it possible to apply importance sampling and control variates by strata in an adaptive manner without the need to track differences in computing cost across strata. By batching the resulting paralleled tasks, the proposed algorithm adjusts the allocation of computing budget only occasionally yet effectively taking full account of decreasing stratum variances. A variety of structural reliability problems with sparse structures lie within the scope of the proposed framework in the sense that stratification with gradual adjustment of computing budget avoids wasting the resource on possibly many empty strata, while importance sampling and control variates improve Monte Carlo methods on non-empty strata. We provide numerical results to illustrate the wide applicability and effectiveness of the proposed framework.
Highlights An adaptive framework of stratified sampling is proposed for reliability analysis. The budget allocation is updated via a dynamic scheme periodically. Adaptive importance sampling and control variates is employed by strata. The proposed method is effective for various complex limit state functions.
Adaptive stratified sampling for structural reliability analysis
https://orcid.org/0000-0001-6603-4914
Abstract An adaptive framework of stratified sampling is established for estimating the probability of failure in structural reliability analysis in combination with variance reduction employed by strata. Among a variety of variance reduction techniques, stratification of Latin hypercube type makes it possible to apply importance sampling and control variates by strata in an adaptive manner without the need to track differences in computing cost across strata. By batching the resulting paralleled tasks, the proposed algorithm adjusts the allocation of computing budget only occasionally yet effectively taking full account of decreasing stratum variances. A variety of structural reliability problems with sparse structures lie within the scope of the proposed framework in the sense that stratification with gradual adjustment of computing budget avoids wasting the resource on possibly many empty strata, while importance sampling and control variates improve Monte Carlo methods on non-empty strata. We provide numerical results to illustrate the wide applicability and effectiveness of the proposed framework.
Highlights An adaptive framework of stratified sampling is proposed for reliability analysis. The budget allocation is updated via a dynamic scheme periodically. Adaptive importance sampling and control variates is employed by strata. The proposed method is effective for various complex limit state functions.
Adaptive stratified sampling for structural reliability analysis
Song, Chenxiao (author) / Kawai, Reiichiro (author)
Structural Safety ; 101
2022-10-03
Article (Journal)
Electronic Resource
English
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