FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386989306> ?p ?o ?g. }

• W4386989306 endingPage "109670" @default.
• W4386989306 startingPage "109670" @default.
• W4386989306 abstract "In structural reliability analysis, the development of an efficient and versatile active learning method applicable to problems of varying complexities remains a challenging task. The critical contribution of this work is the elegant implementation of the Kriging-based importance sampling and dimension reduction techniques within an active learning framework. Specifically, the proposed algorithm employs a dimension reduction technique that considers the contributions of important and unimportant random variables, enabling it to handle high-dimensional problems effectively. To exploit the merits of the adaptive Kriging method, a quasi-optimal importance sampling density is established based on the predictive information provided by the Kriging model. Additionally, a novel learning function allocation (LFA) strategy is proposed to automatically select the appropriate learning function from a portfolio of options. This facilitates the selection of new samples near the limit state surface with significant contributions to the failure probability. Moreover, an efficient stopping criterion is introduced to terminate the active learning process at an appropriate stage effectively. In other words, the proposed method inherits the merits of Adaptive Kriging, Dimension Reduction, and Importance Sampling, hence named AK-DRIS in this study. The overall performance of AK-DRIS is verified through several numerical examples of different complexities." @default.
• W4386989306 created "2023-09-24" @default.
• W4386989306 creator A5010045536 @default.
• W4386989306 creator A5058361224 @default.
• W4386989306 creator A5069470138 @default.
• W4386989306 creator A5083684602 @default.
• W4386989306 creator A5084350058 @default.
• W4386989306 date "2024-01-01" @default.
• W4386989306 modified "2023-10-09" @default.
• W4386989306 title "An efficient and versatile Kriging-based active learning method for structural reliability analysis" @default.
• W4386989306 cites W1572480955 @default.
• W4386989306 cites W1972061973 @default.
• W4386989306 cites W1978031052 @default.
• W4386989306 cites W1980626417 @default.
• W4386989306 cites W1999091229 @default.
• W4386989306 cites W2003732947 @default.
• W4386989306 cites W2007535697 @default.
• W4386989306 cites W2023678279 @default.
• W4386989306 cites W2031295804 @default.
• W4386989306 cites W2045355467 @default.
• W4386989306 cites W2069065855 @default.
• W4386989306 cites W2070748799 @default.
• W4386989306 cites W2078792326 @default.
• W4386989306 cites W2088933359 @default.
• W4386989306 cites W2096285034 @default.
• W4386989306 cites W2236331705 @default.
• W4386989306 cites W2436549090 @default.
• W4386989306 cites W2520879866 @default.
• W4386989306 cites W2731103645 @default.
• W4386989306 cites W2757101479 @default.
• W4386989306 cites W2757374624 @default.
• W4386989306 cites W2794000758 @default.
• W4386989306 cites W2795562280 @default.
• W4386989306 cites W2896416110 @default.
• W4386989306 cites W2900833721 @default.
• W4386989306 cites W2905070828 @default.
• W4386989306 cites W2908794637 @default.
• W4386989306 cites W2920929604 @default.
• W4386989306 cites W2956012286 @default.
• W4386989306 cites W2962998905 @default.
• W4386989306 cites W2971813394 @default.
• W4386989306 cites W2972489092 @default.
• W4386989306 cites W2984163149 @default.
• W4386989306 cites W2988968426 @default.
• W4386989306 cites W3000877573 @default.
• W4386989306 cites W3005874793 @default.
• W4386989306 cites W3011386366 @default.
• W4386989306 cites W3015952220 @default.
• W4386989306 cites W3024106940 @default.
• W4386989306 cites W3080882994 @default.
• W4386989306 cites W3092006514 @default.
• W4386989306 cites W3196918406 @default.
• W4386989306 cites W3204813890 @default.
• W4386989306 cites W3210851548 @default.
• W4386989306 cites W3213526210 @default.
• W4386989306 cites W32980360 @default.
• W4386989306 cites W4200391815 @default.
• W4386989306 cites W4252362670 @default.
• W4386989306 cites W4308977063 @default.
• W4386989306 cites W4321782502 @default.
• W4386989306 cites W4368353523 @default.
• W4386989306 cites W4384340293 @default.
• W4386989306 doi "https://doi.org/10.1016/j.ress.2023.109670" @default.
• W4386989306 hasPublicationYear "2024" @default.
• W4386989306 type Work @default.
• W4386989306 citedByCount "0" @default.
• W4386989306 crossrefType "journal-article" @default.
• W4386989306 hasAuthorship W4386989306A5010045536 @default.
• W4386989306 hasAuthorship W4386989306A5058361224 @default.
• W4386989306 hasAuthorship W4386989306A5069470138 @default.
• W4386989306 hasAuthorship W4386989306A5083684602 @default.
• W4386989306 hasAuthorship W4386989306A5084350058 @default.
• W4386989306 hasConcept C105795698 @default.
• W4386989306 hasConcept C106131492 @default.
• W4386989306 hasConcept C111335779 @default.
• W4386989306 hasConcept C111919701 @default.
• W4386989306 hasConcept C119857082 @default.
• W4386989306 hasConcept C121332964 @default.
• W4386989306 hasConcept C126255220 @default.
• W4386989306 hasConcept C140779682 @default.
• W4386989306 hasConcept C163258240 @default.
• W4386989306 hasConcept C165696696 @default.
• W4386989306 hasConcept C19499675 @default.
• W4386989306 hasConcept C202444582 @default.
• W4386989306 hasConcept C2524010 @default.
• W4386989306 hasConcept C2781395549 @default.
• W4386989306 hasConcept C31972630 @default.
• W4386989306 hasConcept C33676613 @default.
• W4386989306 hasConcept C33923547 @default.
• W4386989306 hasConcept C38652104 @default.
• W4386989306 hasConcept C41008148 @default.
• W4386989306 hasConcept C43214815 @default.
• W4386989306 hasConcept C52740198 @default.
• W4386989306 hasConcept C62520636 @default.
• W4386989306 hasConcept C70518039 @default.
• W4386989306 hasConcept C81692654 @default.
• W4386989306 hasConcept C98045186 @default.
• W4386989306 hasConceptScore W4386989306C105795698 @default.

• next