{"id":"W3181911865","doi":"10.1007/s00158-021-02996-y","title":"Structural uncertainty analysis with the multiplicative dimensional reduction–based polynomial chaos expansion approach","year":2021,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Polynomial chaos; Univariate; Dimensionality reduction; Uncertainty quantification; Polynomial; Mathematics; Applied mathematics; Mathematical optimization; Multiplicative function; Curse of dimensionality; Monte Carlo method; Taylor series; Multivariate statistics; Computer science; Mathematical analysis; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006405389,0.000623478,0.0007518575,0.0008032156,0.0003728904,0.0006513046,0.0008205563,0.0005700847,0.001553277],"category_scores_gemma":[0.001805362,0.0003359034,0.000937907,0.0006299573,0.0008616183,0.001146892,0.001267881,0.001212262,0.0003682035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978452,"about_ca_system_score_gemma":0.0005741196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000882439,"about_ca_topic_score_gemma":0.0007679507,"domain_scores_codex":[0.9994811,0.0001715675,0.00001787492,0.00004773887,0.000244855,0.00003687441],"domain_scores_gemma":[0.9995431,0.0002111382,0.00004907184,0.0000652536,0.0001130319,0.00001836498],"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":[0.00004141173,0.00003084846,0.0002735156,0.00008964423,0.00005278131,0.00006156523,0.00005397555,0.7604113,0.00631759,0.1969488,0.0009772523,0.03474129],"study_design_scores_gemma":[0.000001813391,0.00001336155,0.0000711036,0.000002877006,0.000004998007,0.00001299069,0.000003303594,0.9826224,0.0005326897,0.01623224,0.0004964587,0.00000579581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005676895,0.0001188889,0.9913385,0.0001089719,0.00003183725,0.00001414476,0.00002302579,0.0000372785,0.002650536],"genre_scores_gemma":[0.7332714,0.0007722956,0.2554902,0.0001578352,0.0002381097,0.000183501,0.0001569657,0.0001526044,0.009577113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001553277,"threshold_uncertainty_score":0.005196214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03497195667472834,"score_gpt":0.2931625841514693,"score_spread":0.258190627476741,"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."}}