{"id":"W4400682806","doi":"10.1109/cefc61729.2024.10585612","title":"Physics-Informed Conditional Generative Adversarial Network for Inverse Electromagnetic Problems","year":2024,"lang":"en","type":"article","venue":"","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Adversarial system; Generative grammar; Generative adversarial network; Inverse; Physics; Inverse problem; Artificial intelligence; Computer science; Mathematical analysis; Mathematics; Deep learning; Geometry","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":[0.0007492055,0.0008083277,0.0006556491,0.0003268437,0.0002067932,0.0005482236,0.001046368,0.0009320212,0.00252569],"category_scores_gemma":[0.001944092,0.0004659987,0.0004735475,0.0003525057,0.001098937,0.000704876,0.001130011,0.0016495,0.0004988922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005720908,"about_ca_system_score_gemma":0.0005195428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001576136,"about_ca_topic_score_gemma":0.002320528,"domain_scores_codex":[0.999767,0.00008256887,0.000006931984,0.00005109778,0.00007014348,0.00002227214],"domain_scores_gemma":[0.9992556,0.0005664645,0.00005653357,0.00004629978,0.00005162829,0.00002341595],"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":[0.00001788562,0.000006962899,0.00011778,0.00002290514,0.00001023301,0.00002628979,0.00001170961,0.9807551,0.0008375539,0.01041484,0.0005004591,0.00727836],"study_design_scores_gemma":[0.000001250033,0.00000376681,0.00001826403,0.000001908155,0.000001148841,0.000007252551,9.551439e-7,0.9950178,0.0001788326,0.004564901,0.0002021841,0.00000172596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003657803,0.000133497,0.9939447,0.0001699366,0.00002277305,0.00001363721,0.00005133073,0.0002067729,0.001799619],"genre_scores_gemma":[0.766831,0.0005880498,0.2218085,0.0006145852,0.000128085,0.0002269812,0.0004478282,0.0002635937,0.009091352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00252569,"threshold_uncertainty_score":0.008449256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106487917956158,"score_gpt":0.2153641751322365,"score_spread":0.2047153833366207,"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."}}