{"id":"W4313447095","doi":"10.48550/arxiv.2212.14545","title":"A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Hypergraph; Finite element method; Reynolds number; Computer science; Artificial neural network; Node (physics); Interpolation (computer graphics); Theoretical computer science; Algorithm; Topology (electrical circuits); Mathematics; Artificial intelligence; Discrete mathematics; Physics; Engineering; Mechanics; Structural engineering; Turbulence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003936925,0.0004177847,0.000436723,0.000449885,0.0003995066,0.000487469,0.0008605422,0.001358569,0.001648409],"category_scores_gemma":[0.001660018,0.0002649793,0.0003340486,0.0006164185,0.0006045498,0.0006748376,0.0007572901,0.0009470157,0.0002600597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008010009,"about_ca_system_score_gemma":0.0005819621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008925419,"about_ca_topic_score_gemma":0.007366328,"domain_scores_codex":[0.9998996,0.00004006154,0.000003962924,0.00001855138,0.00002724729,0.00001046339],"domain_scores_gemma":[0.9995376,0.0002846217,0.00002913917,0.00003836428,0.00007943899,0.00003083151],"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":[0.00001566828,0.0000155694,0.0002719733,0.00001603235,0.000009228648,0.00002389423,0.0000154126,0.988739,0.0006418372,0.002330566,0.0004623048,0.00745844],"study_design_scores_gemma":[8.184485e-7,0.000001415827,0.00001442181,7.525181e-7,3.41606e-7,0.000001359017,0.00000115476,0.9990854,0.0000943109,0.000722423,0.00007675924,7.507734e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1698399,0.0008945219,0.8140488,0.002022457,0.0001943892,0.000096269,0.0003959628,0.001977132,0.01053054],"genre_scores_gemma":[0.8301939,0.0003809238,0.1643443,0.0002418151,0.00005506112,0.0001410285,0.0003265921,0.000189657,0.004126838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008925419,"threshold_uncertainty_score":0.01774693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03832285678662496,"score_gpt":0.203681972392032,"score_spread":0.165359115605407,"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."}}