{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005034779,0.0008361154,0.0003747806,0.0006164282,0.0002669872,0.0006524813,0.0005670586,0.0006337186,0.00170171],"category_scores_gemma":[0.001600123,0.0003578673,0.0005409825,0.0004205945,0.0001670093,0.0005000744,0.000357476,0.0007828771,0.0007568932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005636087,"about_ca_system_score_gemma":0.0005258048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0138997,"about_ca_topic_score_gemma":0.01003137,"domain_scores_codex":[0.9998152,0.00004007386,0.00001351798,0.00005673114,0.00005177236,0.00002291516],"domain_scores_gemma":[0.9996194,0.0001828289,0.00005174026,0.00002743157,0.0001103081,0.000008259325],"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":[0.00002548003,0.00002931349,0.0006381777,0.00002374914,0.00002722039,0.00001774557,0.00001247545,0.9681369,0.001247127,0.0003329419,0.0002399999,0.0292688],"study_design_scores_gemma":[8.123129e-7,0.000005848541,0.0001870016,0.000003198013,0.000002139143,0.000003184154,0.000001945833,0.9991726,0.0003843335,0.0001442771,0.00009263714,0.000001891413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08381508,0.0004086065,0.9062951,0.000158633,0.0000539758,0.0001096253,0.0003552507,0.00197212,0.006831551],"genre_scores_gemma":[0.9251601,0.0002278213,0.06825507,0.0000478141,0.00002370164,0.0002381753,0.0006272689,0.00007029713,0.005349725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0138997,"threshold_uncertainty_score":0.0276376,"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."}}