{"id":"W4309226941","doi":"10.1109/cefc55061.2022.9940692","title":"Generalizable DNN based multi-material Hysteresis Modelling","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Transformer; Computer science; Actuator; Representation (politics); Finite element method; Point (geometry); Hysteresis; Operating point; Magnetic hysteresis; Electronic engineering; Control engineering; Mechanical engineering; Magnetization; Voltage; Electrical engineering; Engineering; Artificial intelligence; Structural engineering; Physics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000325751,0.0002955726,0.000314823,0.0001643072,0.000790461,0.0002528415,0.0006067366,0.00008667984,0.01264081],"category_scores_gemma":[0.00001984861,0.0003026596,0.00009382601,0.0003315205,0.00006126853,0.00009667028,0.0001234353,0.0003040083,0.0001396314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071532,"about_ca_system_score_gemma":0.0002384227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008032424,"about_ca_topic_score_gemma":0.00004702536,"domain_scores_codex":[0.9973728,0.0003040538,0.0005115956,0.000704434,0.0005133501,0.0005937445],"domain_scores_gemma":[0.9989797,0.0001302243,0.000212088,0.0004174153,0.000123738,0.0001368626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003015267,0.0003226123,0.000006721026,0.0000385295,0.000008096757,0.00001023534,0.0002391145,0.2128801,0.7749212,0.003641319,0.00481682,0.002813709],"study_design_scores_gemma":[0.0009013088,0.002296774,0.00002127133,0.00001431164,0.00002857651,0.000009944712,0.00007490961,0.9113515,0.080975,0.001107336,0.002794408,0.0004246732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8163518,0.00007387741,0.1761013,0.002566201,0.001273989,0.0007659086,0.0001253677,0.0002731972,0.002468377],"genre_scores_gemma":[0.9814948,0.00001439979,0.01435432,0.001552679,0.000164283,0.0004720333,0.0001021268,0.00003564586,0.001809684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6984714,"threshold_uncertainty_score":0.9999425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04135564424512689,"score_gpt":0.2526613816982787,"score_spread":0.2113057374531518,"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."}}