{"id":"W3189229195","doi":"10.1109/tap.2022.3188403","title":"A Parallel Boundary Element Method for the Electromagnetic Analysis of Large Structures With Lossy Conductors","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Advanced Micro Devices","keywords":"Computer science; Solver; Scalability; Parallel computing; Boundary element method; Lossy compression; Electromagnetic field; Scheduling (production processes); Computational science; Multi-core processor; Workload; Electrical conductor; Boundary (topology); Finite element method; Mathematical optimization; Physics; Mathematics","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":[0.0004066696,0.0006841662,0.0005915984,0.0004765917,0.0006238635,0.0005705291,0.001088406,0.0008330002,0.00429646],"category_scores_gemma":[0.001452065,0.0003679307,0.0005207284,0.000537951,0.0004498796,0.0009491113,0.0008672903,0.001175258,0.001686699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004000032,"about_ca_system_score_gemma":0.001067658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00192905,"about_ca_topic_score_gemma":0.002085578,"domain_scores_codex":[0.999729,0.00005551237,0.00001021638,0.00003431993,0.0001496887,0.0000212707],"domain_scores_gemma":[0.9996623,0.0001395031,0.0000227407,0.00005067046,0.00009812575,0.00002673907],"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":[0.0001208482,0.0001583306,0.0008696837,0.0002602081,0.00005350269,0.000231107,0.0002373777,0.6803581,0.05655828,0.06842764,0.007625958,0.1850989],"study_design_scores_gemma":[0.00002018397,0.00001264847,0.00003984447,0.000007440204,0.000003372948,0.00003730309,0.00001272103,0.985333,0.002880864,0.006115729,0.00553135,0.000005608646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00271901,0.0000839983,0.9941674,0.0001133252,0.00004358514,0.00003177286,0.00003688153,0.0004165102,0.002387451],"genre_scores_gemma":[0.0576692,0.0001666476,0.937934,0.00010345,0.0000359936,0.0002162033,0.0001529141,0.0003411396,0.003380491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00429646,"threshold_uncertainty_score":0.01437306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009520513023583888,"score_gpt":0.2641990736261564,"score_spread":0.2546785606025725,"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."}}