{"id":"W1984796933","doi":"10.1016/j.cam.2006.01.044","title":"Interior penalty discontinuous Galerkin method for Maxwell's equations: Energy norm error estimates","year":2006,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Mathematics; Discontinuous Galerkin method; Norm (philosophy); Gravitational singularity; Maxwell's equations; Penalty method; A priori and a posteriori; Galerkin method; Mathematical analysis; Error analysis; Space (punctuation); Applied mathematics; Finite element method; Mathematical optimization; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003900814,0.0002156422,0.0004732374,0.0001478058,0.00008418527,0.00006595431,0.0001522371,0.0000588634,0.00001109107],"category_scores_gemma":[0.0001480551,0.0001827496,0.0001142742,0.0001455878,0.00005618889,0.0001204058,0.00003277979,0.0001275136,0.000001527956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005203727,"about_ca_system_score_gemma":0.00002918308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.665312e-7,"about_ca_topic_score_gemma":5.731059e-7,"domain_scores_codex":[0.9984602,0.00001319441,0.0009183006,0.0001158671,0.0003127528,0.0001796875],"domain_scores_gemma":[0.9958434,0.003364771,0.0004201021,0.00007875011,0.0002196761,0.00007334907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001863959,0.00009192822,0.000001621743,0.0003339602,0.00007443316,0.000002043738,0.0001344368,0.7323059,0.001256809,0.2465056,0.0005474124,0.01872726],"study_design_scores_gemma":[0.0003018019,0.00003526316,0.00001760267,0.00005968003,0.00004304678,0.00006632969,0.00006533782,0.4772158,0.0008231479,0.5207716,0.0004825857,0.0001178331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001876125,0.0002300765,0.996564,0.0001338404,0.0001447053,0.0001674936,0.00001978387,0.00005740325,0.0008065403],"genre_scores_gemma":[0.07089666,0.000007307541,0.928697,0.00005955672,0.0002105874,0.0000288094,0.00002134697,0.00004368787,0.00003501648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.274266,"threshold_uncertainty_score":0.7452311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084324844010306,"score_gpt":0.3035484782867031,"score_spread":0.2827052298466001,"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."}}