{"id":"W4378603287","doi":"10.1016/j.jsv.2023.117816","title":"Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning","year":2023,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"National Nuclear Security Administration; Sandia National Laboratories; U.S. Department of Energy","keywords":"Overfitting; Nonlinear system; Aerodynamics; Computer science; Aeroelasticity; Limit cycle; Limit (mathematics); Flutter; Surrogate model; Bayesian probability; Algorithm; Mathematics; Machine learning; Artificial intelligence; Engineering; Physics; Artificial neural network","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":[0.001598881,0.00007906544,0.0002140435,0.000346854,0.0001765139,0.0001355012,0.00008946726,0.00006185727,0.000008420294],"category_scores_gemma":[0.001344528,0.00006013781,0.00004777331,0.0005198287,0.00006306249,0.0004229695,0.00003820056,0.0001526437,0.000001964589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001807409,"about_ca_system_score_gemma":0.00006572966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004587358,"about_ca_topic_score_gemma":0.000005381419,"domain_scores_codex":[0.9987238,0.00007172447,0.000572273,0.0001215603,0.0004019443,0.0001086687],"domain_scores_gemma":[0.9986453,0.0005813732,0.0003885432,0.00008208349,0.0002262906,0.00007639196],"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":[0.00003197042,0.00003035666,0.01798162,0.00002981386,0.00002767539,0.000009662942,0.001276525,0.9614792,0.01255883,0.001434594,0.0000274858,0.005112204],"study_design_scores_gemma":[0.0002221688,0.0001000711,0.004550171,0.00005084718,0.0000234501,0.00004525976,0.0008138947,0.9894125,0.00009103089,0.004460621,0.0001595796,0.00007034376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.645135,0.00005834549,0.3544244,0.0001105479,0.0001421963,0.00003713026,0.000003255129,0.000009679645,0.00007933204],"genre_scores_gemma":[0.9693863,0.000108895,0.03028352,0.000009311593,0.0001657459,1.263734e-7,0.000002229641,0.000007700974,0.00003614818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3242513,"threshold_uncertainty_score":0.2452349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08458936151880472,"score_gpt":0.3329168401800889,"score_spread":0.2483274786612842,"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."}}