{"id":"W4406785163","doi":"10.48550/arxiv.2501.13108","title":"Physics-Informed Neural Networks for microflows: Rarefied Gas Dynamics in Cylinder Arrays","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Lattice Boltzmann Simulation Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Gas dynamics; Dynamics (music); Cylinder; Artificial neural network; Physics; Mechanics; Statistical physics; Computer science; Mechanical engineering; Engineering; Artificial intelligence; Acoustics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003973955,0.0004565776,0.0003125556,0.0002502316,0.0003086269,0.0003435434,0.0006268764,0.0006342711,0.001114847],"category_scores_gemma":[0.001470885,0.0002502586,0.000260832,0.0002178576,0.0003782207,0.0005714066,0.0004513919,0.0007444076,0.0001509472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006441428,"about_ca_system_score_gemma":0.0007246127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01458931,"about_ca_topic_score_gemma":0.01142516,"domain_scores_codex":[0.9999232,0.00001952795,0.000003887697,0.00001970958,0.00001961566,0.00001387372],"domain_scores_gemma":[0.9996411,0.0002138414,0.00003018828,0.00002639346,0.00006918303,0.00001936078],"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":[0.00002457294,0.00001409599,0.0006458187,0.00001171264,0.000006415633,0.00001657985,0.0000122703,0.9902064,0.001020323,0.00102511,0.0002161912,0.006800462],"study_design_scores_gemma":[7.084947e-7,0.000001879457,0.00003198444,5.195187e-7,2.702895e-7,7.941169e-7,9.321828e-7,0.9995468,0.0001880219,0.0001985801,0.00002888142,5.686989e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5803781,0.0005635329,0.4091923,0.0009148748,0.0001539046,0.00006905403,0.0002939862,0.001192698,0.007241695],"genre_scores_gemma":[0.9556407,0.000106656,0.04170093,0.00009997172,0.00002162735,0.0000534259,0.0002492463,0.00005159514,0.002075827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01458931,"threshold_uncertainty_score":0.02900881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03774312257949377,"score_gpt":0.2808834682706284,"score_spread":0.2431403456911347,"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."}}