{"id":"W4388720813","doi":"10.1109/epeps58208.2023.10314954","title":"Fast Electromagnetic Analysis of Multiscale Interconnect Networks using MultiAIM","year":2023,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interconnection; Computer science; Multigrid method; Fast Fourier transform; Electronic engineering; Electromagnetic simulation; Computational electromagnetics; Electromagnetic compatibility; Electromagnetic environment; Computational science; Electromagnetic field; Algorithm; Engineering; Telecommunications; Mathematics; Partial differential equation; 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":[0.0003747694,0.0003415231,0.0003205213,0.0005062239,0.0002852181,0.0005336941,0.0006335301,0.0006022508,0.001586702],"category_scores_gemma":[0.0008876632,0.0001638039,0.0004154128,0.0003663918,0.000390497,0.0007174022,0.0006392761,0.0005691048,0.0004566153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003812316,"about_ca_system_score_gemma":0.0003583483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008241039,"about_ca_topic_score_gemma":0.0009790373,"domain_scores_codex":[0.9998519,0.00003364488,0.000004650742,0.00001459107,0.00008116931,0.00001397243],"domain_scores_gemma":[0.9997553,0.0001053757,0.00002839041,0.00003925431,0.00005742041,0.00001428545],"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":[0.00005057438,0.00004367487,0.001008853,0.0001249262,0.00004097025,0.0001825959,0.0001729783,0.7703221,0.03585801,0.1085918,0.001717565,0.0818859],"study_design_scores_gemma":[0.000002461921,0.00000675019,0.00007594065,0.00000250666,0.000001006076,0.0000134866,0.000003929234,0.9933089,0.0009991545,0.004645391,0.0009377599,0.000002720336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01611795,0.000113149,0.9805409,0.00009858478,0.00003052482,0.00001557896,0.00002845905,0.0003253954,0.002729557],"genre_scores_gemma":[0.3798121,0.0002552801,0.6153558,0.00008381424,0.00004980115,0.0001122148,0.0001218076,0.0001904879,0.004018741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001586702,"threshold_uncertainty_score":0.005308032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122567101634666,"score_gpt":0.2619059357885424,"score_spread":0.2506802647721958,"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."}}