{"id":"W4322731021","doi":"10.1109/tmag.2023.3247023","title":"Physics-Informed Neural Networks for Inverse Electromagnetic Problems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Electromagnetism; Inverse problem; Solver; Artificial neural network; Inverse; Set (abstract data type); Computer science; A priori and a posteriori; Electromagnetic coil; Field (mathematics); Electromagnetics; Electromagnetic field; Magnetostatics; Applied mathematics; Magnetic field; Physics; Artificial intelligence; Mathematical analysis; Mathematics; Geometry; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006460691,0.0002450337,0.0001995963,0.00009791508,0.0002862166,0.00006665588,0.000156076,0.00007595433,0.0004028817],"category_scores_gemma":[8.92863e-7,0.0002451552,0.0002338554,0.0005507952,0.00007088789,0.0001174602,0.000001716263,0.0003274892,0.00008993564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002473796,"about_ca_system_score_gemma":0.00004148418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001627719,"about_ca_topic_score_gemma":0.00001317208,"domain_scores_codex":[0.9986838,0.00002854093,0.0002774005,0.0003023827,0.0001649752,0.0005428935],"domain_scores_gemma":[0.9993119,0.0001305219,0.00007255932,0.0002704751,0.00006866657,0.0001458348],"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":[0.0000575462,0.0001237272,0.00001997247,0.00002043862,0.00004050064,5.914309e-7,0.0001230423,0.8528493,0.0006314316,0.0005587028,0.005975724,0.139599],"study_design_scores_gemma":[0.0009112171,0.0005377744,0.00002779461,0.00001350746,0.00006942097,0.000001399473,0.00007323423,0.9907663,0.002454242,0.001684923,0.00317303,0.000287109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05523596,0.00001495047,0.9400091,0.0004535022,0.001500204,0.0009409905,0.00006341547,0.0003414767,0.001440467],"genre_scores_gemma":[0.9911392,0.00005134223,0.0006533358,0.0001740105,0.0005462846,0.0003426731,0.00005230307,0.00005284144,0.006987984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9393557,"threshold_uncertainty_score":0.9997138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02606488096901369,"score_gpt":0.257521841741794,"score_spread":0.2314569607727804,"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."}}