{"id":"W2983118500","doi":"10.1109/tmag.2019.2942804","title":"A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Vibration Control and Rheological Fluids","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Magnetorheological fluid; Computer science; Backpropagation; Compression (physics); Perceptron; Multilayer perceptron; Materials science; Artificial intelligence; Structural engineering; Composite material; Engineering","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":[0.0002949079,0.0004111262,0.0003482756,0.0002153061,0.000195284,0.0003799825,0.0005013522,0.0008643072,0.0007784056],"category_scores_gemma":[0.0006674382,0.0002843514,0.0003797763,0.0002159031,0.0003005923,0.0005107022,0.0002541748,0.0005172717,0.0001630458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003025941,"about_ca_system_score_gemma":0.0004964127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003784697,"about_ca_topic_score_gemma":0.003615188,"domain_scores_codex":[0.9998932,0.00003820396,0.000006454782,0.0000226335,0.0000301504,0.000009327509],"domain_scores_gemma":[0.9998043,0.00010802,0.00002380366,0.00001309568,0.00004519735,0.000005589285],"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":[0.00001869738,0.00001679643,0.0002298972,0.0000202617,0.000006816411,0.00003086168,0.00001634813,0.9896436,0.00315622,0.00046438,0.0000868374,0.006309358],"study_design_scores_gemma":[3.627233e-7,0.000002730695,0.00003521668,6.404788e-7,4.950171e-7,0.000001496204,6.898301e-7,0.9996372,0.0002441125,0.00004222578,0.00003406411,7.286906e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.239221,0.0005726982,0.749036,0.0002901351,0.00005613787,0.0000694076,0.0001726925,0.0005712862,0.01001073],"genre_scores_gemma":[0.9364043,0.0003163529,0.0570672,0.00003561783,0.00001600734,0.0001742151,0.0001480698,0.0000327815,0.005805448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003784697,"threshold_uncertainty_score":0.007525325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880698491701712,"score_gpt":0.2223578696511565,"score_spread":0.2035508847341394,"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."}}