{"id":"W2584862016","doi":"10.1007/s10704-016-0167-x","title":"Non-differentiable energy minimization for cohesive fracture","year":2017,"lang":"en","type":"article","venue":"International Journal of Fracture","topic":"Numerical methods in engineering","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Solver; Minification; Computation; Discontinuity (linguistics); Traction (geology); Mathematics; Nonlinear system; Differentiable function; Augmented Lagrangian method; Energy minimization; Displacement (psychology); Mathematical optimization; Applied mathematics; Computer science; Algorithm; Mathematical analysis","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":[0.00009277228,0.0001479972,0.0002219816,0.0001250893,0.0000684193,0.0001610177,0.0007066065,0.0001322451,0.00009962809],"category_scores_gemma":[0.0003146904,0.0001270599,0.0001475978,0.00002160614,0.00002227841,0.0003992642,0.00002846138,0.0002438537,0.000001818283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009460616,"about_ca_system_score_gemma":0.00001965562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005343628,"about_ca_topic_score_gemma":9.713054e-7,"domain_scores_codex":[0.9991461,0.000007934653,0.0003123787,0.00008641273,0.0003009569,0.0001462111],"domain_scores_gemma":[0.9988981,0.0001384906,0.000302324,0.0001841989,0.0003961691,0.00008069288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001601785,0.0000764974,0.001478971,0.00006072534,0.001025023,0.00006993923,0.0003187203,0.8746501,0.01150078,0.0004303506,0.06653972,0.04368896],"study_design_scores_gemma":[0.001671887,0.00009913101,0.01547814,0.0002422903,0.00008976703,0.0001130372,0.00004224708,0.1286666,0.09769604,0.006746229,0.7487538,0.0004008024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004909956,0.0002392982,0.9898894,0.0009853591,0.003180869,0.00005439678,0.00001348759,0.00002903756,0.0006981361],"genre_scores_gemma":[0.9420623,0.0001125184,0.0555619,0.000354151,0.001697098,0.000006083876,0.000009283943,0.00004064884,0.0001560677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9371523,"threshold_uncertainty_score":0.5181352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00948002646066617,"score_gpt":0.2854197628334026,"score_spread":0.2759397363727364,"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."}}