{"id":"W3137738592","doi":"10.1111/mice.12655","title":"A knowledge‐enhanced deep reinforcement learning‐based shape optimizer for aerodynamic mitigation of wind‐sensitive structures","year":2021,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Reinforcement learning; Aerodynamics; Computer science; Artificial neural network; Artificial intelligence; Domain knowledge; Shape optimization; Process (computing); Domain (mathematical analysis); Deep learning; Machine learning; Engineering; Aerospace engineering; Mathematics; Structural engineering; Finite element method","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":[0.0005015234,0.0008581296,0.001093515,0.0002668061,0.0002682623,0.0004496537,0.001424613,0.001448071,0.002908127],"category_scores_gemma":[0.001021035,0.0004780762,0.0005292761,0.0002320994,0.0004584267,0.0005156797,0.001043591,0.001404791,0.0006869832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004766981,"about_ca_system_score_gemma":0.0009722225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008120253,"about_ca_topic_score_gemma":0.01012335,"domain_scores_codex":[0.9998322,0.0000307485,0.000008071317,0.00004454143,0.00005043962,0.00003396939],"domain_scores_gemma":[0.9996564,0.000153846,0.0000308183,0.00003082777,0.00009393391,0.00003409538],"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":[0.00008680495,0.00008263892,0.0003193983,0.00003357824,0.00003421094,0.0000525527,0.00001936014,0.9163344,0.002915313,0.001400389,0.001884126,0.07683709],"study_design_scores_gemma":[0.000004504574,0.0000118316,0.00002115168,0.000001248906,0.00000164948,0.000002835545,8.953584e-7,0.9995633,0.0001408288,0.0001775482,0.0000730509,0.000001092296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04542961,0.0005652981,0.947077,0.0002822427,0.0001673879,0.00005722435,0.0001126951,0.001937058,0.004371434],"genre_scores_gemma":[0.8325838,0.000162072,0.159689,0.0005229487,0.00008177595,0.0001438855,0.0003215618,0.0001868204,0.006308162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008120253,"threshold_uncertainty_score":0.01614594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003787149646000442,"score_gpt":0.19159702718897,"score_spread":0.1878098775429696,"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."}}