{"id":"W3034989401","doi":"10.2514/6.2020-3058","title":"Towards an hybrid computational strategy based on Deep Learning for incompressible flows","year":2020,"lang":"en","type":"article","venue":"AIAA AVIATION 2020 FORUM","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Council of Prairie and Pacific University Libraries","funders":"Agence Nationale de la Recherche","keywords":"Solver; Computer science; Artificial neural network; Compressibility; Applied mathematics; Acceleration; Incompressible flow; Convolutional neural network; Computational fluid dynamics; Flow (mathematics); Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Mechanics; Physics; Classical mechanics","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":[0.0004460551,0.0006573269,0.0005075234,0.0004252363,0.0002973319,0.0007188504,0.001357931,0.0007703284,0.001682741],"category_scores_gemma":[0.000879804,0.0003591281,0.0004200848,0.0002924047,0.0005110487,0.0009021196,0.001341211,0.0009528143,0.0004726807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006515737,"about_ca_system_score_gemma":0.0008529116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005575334,"about_ca_topic_score_gemma":0.006519676,"domain_scores_codex":[0.9998457,0.00003264099,0.000007765518,0.00002848008,0.00006046762,0.00002489135],"domain_scores_gemma":[0.9997346,0.0001013133,0.00002747238,0.00003882875,0.00007155533,0.00002621298],"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":[0.00008327502,0.00009020284,0.0008660113,0.00006314059,0.00005241497,0.0000671973,0.00005730848,0.9051023,0.009911196,0.0159027,0.0009246067,0.06687967],"study_design_scores_gemma":[0.000001856336,0.000007560022,0.00002335089,0.000001765874,0.00000140633,0.00000360681,0.000001728722,0.9984348,0.0004451058,0.0008888959,0.0001887075,0.000001184869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02952646,0.0002228412,0.9660106,0.0001913567,0.00003384237,0.00003672735,0.00004009536,0.0005499893,0.003388053],"genre_scores_gemma":[0.5165328,0.0002113093,0.4764573,0.0002562549,0.00004918877,0.0001769227,0.0002107659,0.0001892446,0.005916217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005575334,"threshold_uncertainty_score":0.01108575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02013347939386225,"score_gpt":0.2681650172900993,"score_spread":0.248031537896237,"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."}}