{"id":"W4230620168","doi":"10.1109/pesgm46819.2021.9637925","title":"A Novel Linking-Domain Extraction Decomposition Method for Parallel Electromagnetic Transient Simulation of Large-Scale AC/DC Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Power &amp; Energy Society General Meeting (PESGM)","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Gaussian elimination; Block matrix; LU decomposition; Domain decomposition methods; Matrix (chemical analysis); Algorithm; Matrix decomposition; Computer science; Diagonal matrix; Eigendecomposition of a matrix; Inverse; Sparse matrix; Iterative method; Diagonal; Mathematics; Eigenvalues and eigenvectors; Physics; Finite element method","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003315232,0.0004930938,0.00041235,0.0003864222,0.0004033304,0.0004383994,0.0006311411,0.0005866099,0.003418949],"category_scores_gemma":[0.0006876424,0.000277368,0.0005077063,0.0004892759,0.0003159398,0.0007992976,0.0006029814,0.0007914667,0.0006573425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003037885,"about_ca_system_score_gemma":0.0007502864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002316045,"about_ca_topic_score_gemma":0.002865467,"domain_scores_codex":[0.9998792,0.00003080424,0.000005789695,0.00001675561,0.00005580508,0.00001152043],"domain_scores_gemma":[0.9998316,0.00006135276,0.00001511571,0.00002204859,0.0000554681,0.00001443587],"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":[0.00005094886,0.00007268901,0.0006653212,0.0001323632,0.0000404456,0.0001515372,0.0001136395,0.8397686,0.02030947,0.04130452,0.0032122,0.09417807],"study_design_scores_gemma":[0.000003109305,0.000004483081,0.00002253316,0.000002207781,0.000001450436,0.00001162389,0.000004538324,0.9971649,0.0007422754,0.0009977712,0.001042746,0.000002352808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002703591,0.00003236793,0.9951922,0.00004116057,0.00001781737,0.00002279081,0.00002437,0.0001991813,0.001766542],"genre_scores_gemma":[0.145219,0.0001654691,0.8494391,0.00007266246,0.00002775796,0.0002037463,0.0002072752,0.0002053191,0.004459736],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003418949,"threshold_uncertainty_score":0.01143754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01463906212716513,"score_gpt":0.3118627321182101,"score_spread":0.297223669991045,"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."}}