{"id":"W3111572758","doi":"10.13111/2066-8201.2020.12.4.2","title":"Artificial Neural Networks-Extended Great Deluge Model to predict Actuators Displacements for a Morphing Wing Tip System","year":2020,"lang":"en","type":"article","venue":"INCAS BULLETIN","topic":"Aeroelasticity and Vibration Control","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; École de Technologie Supérieure; Polytechnique Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Morphing; Actuator; Wing; Engineering; Structural engineering; Wing twist; Aerodynamics; Mechanical engineering; Acoustics; Angle of attack; Computer science; Aerospace engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006403744,0.000879859,0.0007382217,0.000506697,0.0004126958,0.0008660466,0.0009472169,0.001681695,0.002512809],"category_scores_gemma":[0.00121398,0.0005019858,0.0006611894,0.0003278464,0.0005184871,0.0004066756,0.0006130344,0.001197684,0.0003349364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009340536,"about_ca_system_score_gemma":0.0008859434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02874522,"about_ca_topic_score_gemma":0.0156368,"domain_scores_codex":[0.9998162,0.00004613701,0.00001632506,0.00004992227,0.00003517223,0.0000362148],"domain_scores_gemma":[0.9994733,0.000317085,0.00004061959,0.00001047912,0.0001408243,0.00001776296],"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":[0.00002505946,0.00001500004,0.0003898436,0.00001772446,0.00001410734,0.00003377849,0.00001011374,0.9949843,0.0002348465,0.0002622261,0.0001295194,0.003883463],"study_design_scores_gemma":[0.000001092545,0.000004468308,0.00004150657,0.00000131455,0.000001267984,9.1907e-7,0.000001401334,0.9998387,0.00003139145,0.00004628339,0.00003057996,8.920446e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.303328,0.002870932,0.6725958,0.001274919,0.0004460698,0.0002100119,0.000560941,0.001272732,0.01744061],"genre_scores_gemma":[0.9744071,0.0003478697,0.01809913,0.0001240888,0.00003011481,0.0002604671,0.0002959822,0.00002643714,0.006408928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02874522,"threshold_uncertainty_score":0.05715579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02308531378960834,"score_gpt":0.2232971403823416,"score_spread":0.2002118265927333,"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."}}