{"id":"W1903171476","doi":"10.1002/nme.4786","title":"An updated Lagrangian method with error estimation and adaptive remeshing for very large deformation elasticity problems","year":2014,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite element method; Lagrangian; Hyperelastic material; Lagrange multiplier; Projection (relational algebra); Computer science; Elasticity (physics); Applied mathematics; Distortion (music); Adaptive mesh refinement; Mathematical optimization; Algorithm; Mathematics; Structural engineering; Engineering; Computational science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002026633,0.0002591063,0.000366292,0.0003268518,0.00009163153,0.0001189343,0.0002670797,0.0001045977,0.000004547344],"category_scores_gemma":[0.001238572,0.0002350652,0.00007701042,0.0002389917,0.00001845061,0.0007335821,0.0000285959,0.0003723305,5.265907e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002378521,"about_ca_system_score_gemma":0.00001543769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001430463,"about_ca_topic_score_gemma":9.498269e-7,"domain_scores_codex":[0.9983711,0.0001566579,0.0006003012,0.0002325583,0.0002911244,0.0003482379],"domain_scores_gemma":[0.9971239,0.002156332,0.0001686344,0.000110723,0.0002872256,0.0001531603],"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":[0.00007999194,0.00003663851,0.00002438592,0.00007759048,0.00007761841,9.800301e-7,0.0002620724,0.8773151,0.001309243,0.005102561,0.000008868539,0.1157049],"study_design_scores_gemma":[0.0009600637,0.0002409591,0.0002025045,0.0001522264,0.00003216238,0.0001020441,0.00006162796,0.9637989,0.001518862,0.03143222,0.00121469,0.0002836967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001870533,0.0000383673,0.9964247,0.00009037288,0.0009315416,0.0004115816,0.00002313217,0.0001807209,0.00002904702],"genre_scores_gemma":[0.106309,0.000004467172,0.8932115,0.00005442862,0.0002125163,0.0001028342,0.00003073964,0.00007192494,0.000002619581],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1154212,"threshold_uncertainty_score":0.958568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02494188062988753,"score_gpt":0.3778382608264159,"score_spread":0.3528963801965284,"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."}}