{"id":"W3187020323","doi":"10.2514/6.2021-3247","title":"Aircraft Engine Performance Model Identification using Artificial Neural Networks","year":2021,"lang":"en","type":"article","venue":"AIAA Propulsion and Energy 2021 Forum","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Artificial neural network; Climb; Takeoff; Computer science; Flight simulator; MATLAB; Avionics; Data modeling; Takeoff and landing; Range (aeronautics); Simulation; Thrust; Machine learning; Engineering; Automotive engineering; Aerospace engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004567821,0.0001283836,0.0001317998,0.00006073033,0.0001440777,0.00004352121,0.00006150067,0.0001435946,0.00003120157],"category_scores_gemma":[0.000007823621,0.0001313337,0.00003601435,0.0002274718,0.00002751472,0.0001969732,0.0000587256,0.0001342168,0.000004253171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000275799,"about_ca_system_score_gemma":0.00001141749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007437147,"about_ca_topic_score_gemma":0.00003272527,"domain_scores_codex":[0.9992255,0.00001085649,0.0002135432,0.0001981723,0.0000933573,0.0002585855],"domain_scores_gemma":[0.9996774,0.000005707456,0.00003199872,0.0001868938,0.0000497088,0.00004825166],"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":[0.000004625834,0.00001600748,0.0002560485,0.00001600387,0.00001140979,0.0000051268,0.00001933482,0.715251,0.02670898,0.003548902,0.0003930154,0.2537695],"study_design_scores_gemma":[0.00009050809,0.00001184183,0.0001014732,0.00001357555,0.00001059209,0.00001493399,0.00006930233,0.9394912,0.05867807,0.0005517078,0.0008220114,0.0001448517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6683961,0.001287829,0.3282543,0.000999104,0.000592554,0.00003738893,0.000003694897,0.0002383319,0.0001908162],"genre_scores_gemma":[0.9970052,0.0006699879,0.001416474,0.000114904,0.00009360629,0.00001153031,0.00005598736,0.00002621636,0.0006060452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3286092,"threshold_uncertainty_score":0.5355632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00989212664998044,"score_gpt":0.2026980490436403,"score_spread":0.1928059223936599,"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."}}