{"id":"W1964121490","doi":"10.1109/iembs.2011.6090128","title":"Multi-GPU accelerated three-dimensional FDTD method for electromagnetic simulation","year":2011,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Information and Communications Technology; Vector Institute","keywords":"Finite-difference time-domain method; CUDA; Supercomputer; Computer science; Computational science; Parallel computing; General-purpose computing on graphics processing units; Computational electromagnetics; Speedup; Code (set theory); Computer graphics (images); Electromagnetic field; Physics; Optics; Graphics; Set (abstract data type)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002541805,0.0002026332,0.0002257682,0.0001047554,0.00006797686,0.00001787017,0.0001159564,0.0001243265,0.001598974],"category_scores_gemma":[0.00010628,0.0001857822,0.00009175367,0.0003042686,0.00001718645,0.00009440674,0.00001365512,0.0001266131,0.00004296927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003563553,"about_ca_system_score_gemma":0.00001532277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002722216,"about_ca_topic_score_gemma":0.00002241032,"domain_scores_codex":[0.9988874,0.00006250357,0.0003126487,0.0002420957,0.000127554,0.000367832],"domain_scores_gemma":[0.9990764,0.0004710341,0.00003202977,0.0001889165,0.0001165314,0.0001150745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002067793,0.0002380984,0.0003486908,0.0000737023,0.000146533,0.000003019222,0.0002187713,0.3475097,0.4647396,0.001374473,0.0007728544,0.1843678],"study_design_scores_gemma":[0.0008608964,0.0003590921,0.006842095,0.00000437169,0.000030031,0.000002565308,0.000002475283,0.9612918,0.02820514,0.001449414,0.0007070797,0.0002449715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0392228,0.000174531,0.9569638,0.00002237754,0.000183191,0.0004757977,0.000002707256,0.0005959849,0.002358772],"genre_scores_gemma":[0.3353464,0.000001907639,0.6640198,0.0001284255,0.00003718553,0.00004735194,0.00001042585,0.00003988086,0.0003687216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6137822,"threshold_uncertainty_score":0.9993137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07761051579121289,"score_gpt":0.3329449865103641,"score_spread":0.2553344707191512,"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."}}