{"id":"W4205812124","doi":"10.2514/6.2022-1417","title":"Robust Neurocontrol for Autonomous Dynamic Soaring","year":2022,"lang":"en","type":"article","venue":"AIAA SCITECH 2022 Forum","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Robustness (evolution); Computer science; Exploit; Neuroevolution; Robust control; Range (aeronautics); Network topology; Control engineering; Control theory (sociology); Artificial neural network; Control system; Artificial intelligence; Engineering; Control (management); 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":[0.000322893,0.0004782993,0.000217882,0.0002771465,0.0002575503,0.0006426843,0.0005350354,0.0004956457,0.00419787],"category_scores_gemma":[0.0008884176,0.0001707521,0.0002460109,0.0001553878,0.0004970346,0.0003236654,0.0007350795,0.0006997828,0.0005275259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005594219,"about_ca_system_score_gemma":0.0005099827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003167819,"about_ca_topic_score_gemma":0.003446153,"domain_scores_codex":[0.9998854,0.00001496919,0.000005540677,0.00002621969,0.00005599473,0.00001182307],"domain_scores_gemma":[0.9998202,0.00007182024,0.00003518422,0.00002016211,0.00004147189,0.00001128187],"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":[0.00005897445,0.0000342275,0.0002760504,0.00008165443,0.0000350453,0.0001249187,0.00005176076,0.8361124,0.01524454,0.03186417,0.002492981,0.1136233],"study_design_scores_gemma":[0.000004315483,0.00003052245,0.00008678964,0.000006743522,0.000003093174,0.00001670025,0.000004418151,0.9934933,0.00103501,0.003718234,0.001597306,0.000003561491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01695902,0.000540586,0.9601271,0.0002102974,0.00007919411,0.0000567296,0.00005160937,0.0008443941,0.02113105],"genre_scores_gemma":[0.8850446,0.0004001979,0.1009942,0.0001057917,0.00005401894,0.0001802115,0.00009935763,0.00009465648,0.01302689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00419787,"threshold_uncertainty_score":0.01404327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00684670894064698,"score_gpt":0.1922869142244345,"score_spread":0.1854402052837875,"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."}}