{"id":"W2217647767","doi":"10.1109/apemc.2013.7360600","title":"Acceleration of the two-dimensional shunt-node TLM method using C++AMP","year":2013,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Massively parallel; Computation; Parallel computing; Computational science; Software; Acceleration; Computer hardware; Operating system; Algorithm; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000307512,0.0004373542,0.0003165567,0.0003298532,0.0003210595,0.0005135941,0.001353373,0.0005795117,0.008134482],"category_scores_gemma":[0.00134144,0.0001647649,0.0003086081,0.0004916457,0.0003133337,0.0005149328,0.0005203332,0.0007458474,0.001993093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003559459,"about_ca_system_score_gemma":0.0007098649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002261089,"about_ca_topic_score_gemma":0.002065217,"domain_scores_codex":[0.9997761,0.00005328793,0.00001160842,0.00001524813,0.0001197781,0.00002401879],"domain_scores_gemma":[0.9994504,0.0002176275,0.00003413422,0.00009831371,0.0001680788,0.00003146355],"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":[0.0004692401,0.0002289827,0.001604238,0.0005281388,0.0000609454,0.0008794414,0.0003812593,0.4323021,0.101578,0.08708197,0.02670991,0.3481758],"study_design_scores_gemma":[0.00004320487,0.00004019394,0.0001609247,0.0000109991,0.000004392563,0.00008688335,0.0000150166,0.973727,0.0131979,0.002180852,0.01051968,0.00001292352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02205616,0.00008156491,0.9544641,0.0001793947,0.000151043,0.00006876981,0.0001524287,0.005995488,0.01685105],"genre_scores_gemma":[0.242724,0.0001331906,0.746103,0.0001410983,0.00004199707,0.000158583,0.0002994275,0.0008262367,0.009572507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008134482,"threshold_uncertainty_score":0.02721256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03436459039416651,"score_gpt":0.3205220834639745,"score_spread":0.286157493069808,"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."}}