{"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 & 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001051081,0.000436487,0.0006051497,0.00008010182,0.000252205,0.00007146936,0.0001725995,0.0004498042,0.0001542834],"category_scores_gemma":[0.00007706362,0.0005006438,0.0006949843,0.0007457531,0.00003939708,0.0001341263,0.00002731374,0.00037891,0.000002504663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001879093,"about_ca_system_score_gemma":0.00006331484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003452058,"about_ca_topic_score_gemma":0.0001322666,"domain_scores_codex":[0.996902,0.0003738847,0.0008786987,0.0006291992,0.0004165068,0.0007997488],"domain_scores_gemma":[0.9979473,0.0008834685,0.0002224492,0.0004128872,0.0003468007,0.0001870338],"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":[0.00003725137,0.0001147148,0.000007264441,0.00003855856,0.00009888878,4.863802e-7,0.0005355043,0.5540952,0.4420735,0.00009581151,0.0003816363,0.002521162],"study_design_scores_gemma":[0.001508505,0.0001697172,0.0001338688,0.00008307616,0.0001361436,0.00001619317,0.00008711581,0.9355879,0.04102447,0.0005477206,0.02019187,0.0005134667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1385526,0.001368624,0.858257,0.0001857269,0.0008436381,0.0002173499,0.00001952235,0.0001674205,0.0003881319],"genre_scores_gemma":[0.2169885,0.0002256712,0.7808465,0.0003833306,0.0005409063,0.00009047167,0.0003036558,0.0001102906,0.0005106892],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.401049,"threshold_uncertainty_score":0.9997445,"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."}}