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| Subset simulation method with mixed sampler |
| Received:May 25, 2023 Revised:July 20, 2023 |
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| DOI：10.7511/jslx20230525005 |
| KeyWord:subset simulation Monte Carlo simulation slice sampling conditional sampling |
| Author | Institution |
| 廖子涵 |
浙江大学 伊利诺伊大学厄巴纳香槟校区联合学院, 海宁 ;浙江大学 建筑工程学院, 杭州 |
| 李宾宾 |
浙江大学 伊利诺伊大学厄巴纳香槟校区联合学院, 海宁 ;浙江大学 建筑工程学院, 杭州 |
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| Abstract: |
| Subset simulation method is a widely used Monte Carlo integration method in failure probability estimation.Compared with direct Monte Carlo integration method,it greatly reduces the number of function calls.Subset simulation method generally uses a single sampler,but different samplers have different application ranges.If a single elliptical slice sampler is used,its ergodicity is better,but the number of function calls is higher;When using single adaptive conditional sampling,its sampling efficiency is high,but it is easy for the samples to fall into a local extremum.When a single sampler faces different problems due to its own characteristics,the integration results of failure probability may deviate,and the simulation effect is unstable.In this paper,a mixed sampling set simulation method is proposed for the first time.Elliptic slice sampling is used in the first few layers of subset simulation.At this time,the shrinkage of failure area is limited,and the number of function calls is within an acceptable range.After sampling expansion,the sample can fully explore the parameter space and detect all failure areas more effectively.When the failure area shrinks to a certain limit,adaptive conditional sampling is used.At this time,the seed samples inherit the lower correlation of the previous layers of samples,and on this basis,the samples are multiplied more efficiently through adaptive conditional sampling.In this paper,the numerical integration of four simulation examples in multiple dimensions verifies that the algorithm has good ergodicity of elliptic slice sampler,and the sampling efficiency is between elliptic slice sampling and adaptive conditional sampling.It has good universality for different problems. |
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