{"id":"W2955939954","doi":"10.1016/j.tafmec.2019.102447","title":"Transfer learning enhanced physics informed neural network for phase-field modeling of fracture","year":2019,"lang":"en","type":"preprint","venue":"Theoretical and Applied Fracture Mechanics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Division of Materials Research; Deutscher Akademischer Austauschdienst; University of Notre Dame","keywords":"Artificial neural network; Residual; Computer science; Mathematical optimization; Boundary value problem; Path (computing); Algorithm; Applied mathematics; Mathematics; Artificial intelligence; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001843261,0.0005219052,0.0008355797,0.00003117219,0.0001634066,0.00006423687,0.0002527972,0.0005325511,0.0003470003],"category_scores_gemma":[0.000008438917,0.0004241767,0.0003305363,0.00008142741,0.00006332875,0.00006346702,0.0001546401,0.001843057,0.000002403492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001157757,"about_ca_system_score_gemma":0.00006893843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000388526,"about_ca_topic_score_gemma":1.582148e-7,"domain_scores_codex":[0.9981429,0.00004029461,0.0005205774,0.0005532399,0.0002428466,0.0005001513],"domain_scores_gemma":[0.9988979,0.0003414064,0.00018267,0.0003302192,0.00009565637,0.0001521478],"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":[0.0004039905,0.00005456964,5.731989e-7,0.0001151492,0.0000749095,5.065979e-8,0.0001917073,0.5817439,0.0004125151,0.4014022,0.0001035698,0.01549687],"study_design_scores_gemma":[0.0008442124,0.0001229983,4.478326e-8,0.00008017833,0.0001336438,2.055033e-7,0.0001106464,0.61405,0.01350438,0.3703049,0.0005458407,0.0003029222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04691617,0.00008430505,0.9495991,0.000373648,0.0003812337,0.001015011,0.00004587462,0.00005693183,0.001527717],"genre_scores_gemma":[0.9951965,0.00005614073,0.001495967,0.001000841,0.001637748,0.0001164715,0.0003965764,0.00006843277,0.00003128868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9482804,"threshold_uncertainty_score":0.999821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138993707083939,"score_gpt":0.2605031703557197,"score_spread":0.2491132332848803,"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."}}