{"id":"W4307935090","doi":"10.48550/arxiv.2210.17457","title":"Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Gruppo Nazionale per il Calcolo Scientifico; Ministero dell'Università e della Ricerca; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Computer science; Multigrid method; Grid; Graph partition; Scalability; Graph; Cluster analysis; Theoretical computer science; Inference; Artificial neural network; Algorithm; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001296695,0.0003620679,0.0003683185,0.0004728827,0.0003500469,0.00006500725,0.00170747,0.0001560813,0.00001777284],"category_scores_gemma":[0.000005331704,0.0004130708,0.0002115478,0.00221737,0.0001472138,0.0004319992,0.001858034,0.0007718477,0.000001622158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190988,"about_ca_system_score_gemma":0.000115334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001426603,"about_ca_topic_score_gemma":0.00006821328,"domain_scores_codex":[0.9977675,0.0001292433,0.000279069,0.001201927,0.0001886193,0.0004336341],"domain_scores_gemma":[0.9978358,0.0001057358,0.0004188545,0.001233465,0.0001812925,0.0002248945],"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":[0.00005645613,0.00005731525,0.002625234,0.00001888595,0.00005661583,0.00004586436,0.00007250795,0.9738486,0.00007849171,0.02244622,0.00003680178,0.0006570492],"study_design_scores_gemma":[0.0003314186,0.000112383,0.0009615732,0.00003369687,0.00006109192,0.000008793048,0.00005090543,0.9951119,0.00005538814,0.00268178,0.0001428897,0.0004481527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1495068,0.00006422694,0.8490743,0.00004940948,0.000342306,0.0006869526,0.000022369,0.0001635743,0.00009009051],"genre_scores_gemma":[0.9717448,0.00004965425,0.02779519,0.0001615415,0.00009817589,0.0000107157,0.00004413592,0.00002741474,0.00006834322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8222381,"threshold_uncertainty_score":0.9998321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05739226460969931,"score_gpt":0.2082489897767238,"score_spread":0.1508567251670245,"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."}}