{"id":"W4409213634","doi":"10.1063/5.0267175","title":"Deep reinforcement learning-based active flow control for a tall building","year":2025,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Physics; Flow (mathematics); Reinforcement learning; Reinforcement; Flow control (data); Artificial intelligence; Mechanics; Structural engineering; Engineering; Computer science","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":[0.0006241596,0.0005677709,0.0004862318,0.0002037245,0.0002096625,0.0003792951,0.0004964593,0.0005836227,0.000868827],"category_scores_gemma":[0.001053958,0.0002277747,0.0002650824,0.0001195938,0.0005907327,0.000338286,0.0005771921,0.0007653681,0.0001210898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005049976,"about_ca_system_score_gemma":0.0007637233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00523948,"about_ca_topic_score_gemma":0.00476315,"domain_scores_codex":[0.9998471,0.00003304518,0.000005625214,0.00003722199,0.00003752717,0.00003952825],"domain_scores_gemma":[0.9995071,0.0002568478,0.00008251396,0.00002105821,0.00008507228,0.00004740149],"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":[0.00004063291,0.0000296811,0.0003263258,0.00001644851,0.000007511718,0.0000281641,0.00001546486,0.9835882,0.002341113,0.0008809724,0.0001861506,0.01253925],"study_design_scores_gemma":[0.000002043349,0.00001324457,0.00003473404,8.044091e-7,8.633951e-7,0.00000118308,0.000001098181,0.9995933,0.0001642296,0.000144494,0.00004305757,8.364993e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1078947,0.0002565967,0.8880535,0.0002804522,0.00007337701,0.00003657764,0.00002493938,0.0004596033,0.002920128],"genre_scores_gemma":[0.9795348,0.00004246689,0.01910847,0.00006003516,0.00001516371,0.00002950057,0.00002268483,0.00001220264,0.001174621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00523948,"threshold_uncertainty_score":0.010418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007735918313699373,"score_gpt":0.237834939115507,"score_spread":0.2300990208018076,"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."}}