{"id":"W4396851227","doi":"10.2139/ssrn.4826631","title":"Coupeling of the Finite Element Method with Physicsinformed Neural Networks for the Multi-Fluid Flowproblem","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Finite element method; Artificial neural network; Element (criminal law); Smoothed finite element method; Mixed finite element method; Extended finite element method; Computer science; Applied mathematics; Mathematics; Physics; Boundary knot method; Artificial intelligence; Political science; Boundary element method; Thermodynamics","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001379657,0.000398626,0.0004030237,0.00005159422,0.0004109334,0.0001660098,0.0006988471,0.0001047122,0.00002594226],"category_scores_gemma":[0.000005840815,0.000201866,0.0006573086,0.0001970506,0.00007861355,0.00006084272,0.0003871655,0.005155921,0.000001252352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002198134,"about_ca_system_score_gemma":0.001398992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000879267,"about_ca_topic_score_gemma":0.00008617563,"domain_scores_codex":[0.9970746,0.0001118707,0.0005867267,0.0003439169,0.0003314885,0.0015514],"domain_scores_gemma":[0.9985007,0.0002296105,0.0005574123,0.0004498732,0.0001973907,0.0000650221],"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":[0.00009762077,0.00004511868,0.00008213749,0.00003353147,0.0009070907,1.237611e-7,0.0001526909,0.8864993,0.00002211124,0.03162123,0.0002023064,0.08033675],"study_design_scores_gemma":[0.0006591002,0.0001053882,0.000008242135,0.0001602736,0.0004604863,0.00001479249,0.0006754217,0.9276983,0.0001556223,0.06897627,0.000854056,0.0002320458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005456089,0.002966114,0.9875596,0.001359042,0.001420495,0.001062512,0.00003225394,0.00002288036,0.000121071],"genre_scores_gemma":[0.9937095,0.0006146614,0.001814584,0.00009498443,0.002349307,0.0001485712,0.00002816953,0.00007602611,0.001164166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9882534,"threshold_uncertainty_score":0.9971392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755051334670221,"score_gpt":0.2812085086074806,"score_spread":0.2636579952607784,"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."}}