{"id":"W2165637644","doi":"10.1109/lmwc.2011.2142396","title":"Accelerating Space Mapping Optimization with Adjoint Sensitivities","year":2011,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Components Letters","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Háskólinn í Reykjavík; Rannís; Icelandic Centre for Research","keywords":"Space mapping; Sensitivity (control systems); Surrogate model; Space (punctuation); Adjoint equation; Computer science; Process (computing); Mathematical optimization; Algorithm; Mathematics; Electronic engineering; Engineering; Mathematical analysis","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":[0.0008658568,0.0008055139,0.0005878496,0.0005286522,0.0003042775,0.0006544368,0.0005242355,0.0006848348,0.002357034],"category_scores_gemma":[0.002259362,0.0004189626,0.0005901508,0.0003615353,0.000528387,0.000844039,0.001320265,0.001233645,0.0005582032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003777742,"about_ca_system_score_gemma":0.0009196313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001794917,"about_ca_topic_score_gemma":0.00191932,"domain_scores_codex":[0.9996974,0.00008879221,0.00001197517,0.00003525086,0.0001385021,0.00002809149],"domain_scores_gemma":[0.9995472,0.0002124444,0.00004195857,0.0001018676,0.00007857537,0.00001799007],"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":[0.00003635635,0.00004892972,0.0002959314,0.00003948169,0.00002851913,0.00004652408,0.00005892366,0.894406,0.01657674,0.03068893,0.0009260349,0.05684758],"study_design_scores_gemma":[0.000003772456,0.00001506175,0.00003654645,0.000002059752,0.000002315583,0.00001156294,0.000003468489,0.9901773,0.002937894,0.005927925,0.0008775152,0.000004596404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005605401,0.00001822213,0.992723,0.00005016947,0.00001399891,0.0000127493,0.00001326695,0.0004466699,0.001116535],"genre_scores_gemma":[0.2782552,0.00008097026,0.718588,0.0001049352,0.00002393613,0.0001405323,0.0001015818,0.0003251989,0.002379613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002357034,"threshold_uncertainty_score":0.007885098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04127795918717637,"score_gpt":0.1973410353017166,"score_spread":0.1560630761145402,"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."}}