{"id":"W4401453054","doi":"10.1109/piers62282.2024.10618476","title":"Efficient Uncertainty Quantification with Subspace Pursuit for FDTD Based Microwave Circuit Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Finite-difference time-domain method; Subspace topology; Microwave; Computer science; Microwave imaging; Electronic engineering; Algorithm; Artificial intelligence; Telecommunications; Physics; Engineering; Optics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008471213,0.0005170494,0.0006735213,0.0004770867,0.0003145984,0.0006577759,0.0004949671,0.0006513759,0.0007588494],"category_scores_gemma":[0.001754787,0.0003838295,0.0005211463,0.0004788458,0.0006846756,0.0008012471,0.0008473708,0.0007941274,0.0001438435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005693818,"about_ca_system_score_gemma":0.0006858282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002214744,"about_ca_topic_score_gemma":0.001417862,"domain_scores_codex":[0.999698,0.0001252083,0.00001351279,0.0000280711,0.0001124224,0.00002287811],"domain_scores_gemma":[0.9992753,0.0005220794,0.00006754679,0.00004988507,0.00006809773,0.00001706532],"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":[0.00002401,0.00001268709,0.0001411764,0.00003618793,0.00001463926,0.00002173464,0.0000307827,0.9605594,0.002240337,0.02053708,0.0001924901,0.01618939],"study_design_scores_gemma":[6.558828e-7,0.00000295458,0.00001095613,0.000001032169,5.81722e-7,0.000003501895,0.000001262482,0.9975654,0.0002638035,0.002049352,0.00009911841,0.000001464252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006520503,0.00007107559,0.9926949,0.00005091906,0.000003402186,0.00001029456,0.00002102025,0.00006528448,0.0005625293],"genre_scores_gemma":[0.6270341,0.0004644448,0.3694599,0.00006097462,0.00003189309,0.0001805671,0.0001792722,0.00007658485,0.002512327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002214744,"threshold_uncertainty_score":0.004480004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1529369060538197,"score_gpt":0.3296260338014416,"score_spread":0.1766891277476219,"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."}}