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国际不确定性的量化期刊

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ISSN 打印: 2152-5080

ISSN 在线: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

GAUSSIAN PROCESS ADAPTIVE IMPORTANCE SAMPLING

卷 4, 册 2, 2014, pp. 133-149
DOI: 10.1615/Int.J.UncertaintyQuantification.2013006330
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摘要

The objective is to calculate the probability, PF, that a device will fail when its inputs, x, are randomly distributed with probability density, p (x), e.g., the probability that a device will fracture when subject to varying loads. Here failure is defined as some scalar function, y (x), exceeding a threshold, T. If evaluating y (x) via physical or numerical experiments is sufficiently expensive or PF is sufficiently small, then Monte Carlo (MC) methods to estimate PF will be unfeasible due to the large number of function evaluations required for a specified accuracy. Importance sampling (IS), i.e., preferentially sampling from "important" regions in the input space and appropriately down-weighting to obtain an unbiased estimate, is one approach to assess PF more efficiently. The inputs are sampled from an importance density, p' (x). We present an adaptive importance sampling (AIS) approach which endeavors to adaptively improve the estimate of the ideal importance density, p* (x), during the sampling process. Our approach uses a mixture of component probability densities that each approximate p* (x). An iterative process is used to construct the sequence of improving component probability densities. At each iteration, a Gaussian process (GP) surrogate is used to help identify areas in the space where failure is likely to occur. The GPs are not used to directly calculate the failure probability; they are only used to approximate the importance density. Thus, our Gaussian process adaptive importance sampling (GPAIS) algorithm overcomes limitations involving using a potentially inaccurate surrogate model directly in IS calculations. This robust GPAIS algorithm performs surprisingly well on a pathological test function.

对本文的引用
  1. Eldred Michael S., Swiler Laura P., Adams Brian M., Overview of Selected DOE/NNSA Predictive Science Initiatives: the Predictive Science Academic Alliance Program and the DAKOTA Project (Invited), 53rd AIAA Aerospace Sciences Meeting, 2015. Crossref

  2. Romero Vicente J., Swiler Laura P., Ebeida Mohamed S., Mitchell Scott, A Set of Test Problems and Results in Assessing Method Performance for Calculating Low Probabilities of Failure, 18th AIAA Non-Deterministic Approaches Conference, 2016. Crossref

  3. Swiler Laura P., Eldred Michael S., Adams Brian M., Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment, in Handbook of Uncertainty Quantification, 2017. Crossref

  4. Swiler Laura P., Eldred Michael S., Adams Brian M., Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment, in Handbook of Uncertainty Quantification, 2015. Crossref

  5. Drignei Dorin, Baseski Igor, Mourelatos Zissimos P., Kosova Ervisa, A Random Process Metamodel Approach for Time-Dependent Reliability, Journal of Mechanical Design, 138, 1, 2016. Crossref

  6. Kim Khu-rai, Kim Youngjae, Park Sungyong, Towards Robust Data-Driven Parallel Loop Scheduling Using Bayesian Optimization, 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2019. Crossref

  7. Pannerselvam Kiran, Yadav Deepanshu, Ramu Palaniappan, Scarce Sample-Based Reliability Estimation and Optimization Using Importance Sampling, Mathematical and Computational Applications, 27, 6, 2022. Crossref

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