{"id":"W3037748341","doi":"10.1016/j.ymssp.2020.106980","title":"A novel approach for reliability analysis with correlated variables based on the concepts of entropy and polynomial chaos expansion","year":2020,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Scholarship Council; University of Waterloo; National Natural Science Foundation of China","keywords":"Random variable; Mathematics; Marginal distribution; Polynomial chaos; Entropy (arrow of time); Probability distribution; Applied mathematics; Principle of maximum entropy; Random field; Moment (physics); Multivariate random variable; Monte Carlo method; Mathematical optimization; Algorithm; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00209286,0.0001561636,0.0005117006,0.00006482751,0.0001953228,0.0001639822,0.0002051675,0.0001077988,0.000009620481],"category_scores_gemma":[0.0009588154,0.00007578219,0.00007130254,0.0006900665,0.000109516,0.00007926345,0.00003824252,0.0001312626,2.200004e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001484704,"about_ca_system_score_gemma":0.00008190549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002628035,"about_ca_topic_score_gemma":2.777725e-7,"domain_scores_codex":[0.9981028,0.0001359172,0.0005185278,0.0005109319,0.000557742,0.0001741064],"domain_scores_gemma":[0.9977273,0.001483489,0.0002466134,0.00017568,0.0002282742,0.0001386059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001964651,0.0002796876,0.000630257,0.000607011,0.0001932899,0.000002123347,0.001014783,0.9427712,0.03776237,0.009834419,0.0001809754,0.004759218],"study_design_scores_gemma":[0.0006539718,0.0003638237,0.00006636546,0.00007122614,0.0001523395,0.000002094121,0.0004899531,0.997509,0.0003750355,0.000156172,0.00004857749,0.0001114337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02160761,0.0001396088,0.9773814,0.0002559794,0.0000241251,0.0005118637,0.00002239468,0.00002415283,0.00003287261],"genre_scores_gemma":[0.9878561,6.803032e-7,0.01193619,0.0000910173,0.00004967388,0.00003903565,0.00000377116,0.000009111078,0.00001439492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9662485,"threshold_uncertainty_score":0.3090308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0638101007184458,"score_gpt":0.2806672186688856,"score_spread":0.2168571179504398,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}