{"id":"W4396705678","doi":"10.1007/s10444-024-10139-2","title":"Stray field computation by inverted finite elements: a new method in micromagnetic simulations","year":2024,"lang":"en","type":"article","venue":"Advances in Computational Mathematics","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computational Science and Engineering; Micromagnetics; Computation; Truncation (statistics); Convergence (economics); Field (mathematics); Magnetization; Mathematics; Applied mathematics; Finite field; Algorithm; Energy (signal processing); Computer science; Computational science; Mathematical optimization; Physics; Magnetic field; Discrete mathematics; Pure mathematics","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":[0.0001737414,0.0001596248,0.0002020534,0.0002506474,0.00002124588,0.00005241888,0.00009425907,0.00006495921,0.0001503253],"category_scores_gemma":[0.0001636822,0.000171837,0.00003583423,0.0007806525,0.00001450819,0.0002401735,0.00001419817,0.0002031477,0.00002007509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008348313,"about_ca_system_score_gemma":0.00003401879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006951214,"about_ca_topic_score_gemma":0.00002229399,"domain_scores_codex":[0.9987869,0.00006154688,0.0005621032,0.0001924378,0.0001996644,0.0001973842],"domain_scores_gemma":[0.9962336,0.003573512,0.00003578163,0.00007473207,0.00002732382,0.00005503678],"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":[0.000002596269,0.00004278154,0.00008540827,0.0001485984,0.00000964831,0.000003363423,0.00034679,0.9046137,0.0005610055,0.002128187,0.0003332518,0.09172463],"study_design_scores_gemma":[0.0003178612,0.00006135983,0.0001075675,0.0001248569,0.000007301514,0.000002910354,0.00002548786,0.8191098,0.0001767426,0.1784444,0.001476987,0.0001447474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01039397,0.002915181,0.9849724,0.0002459589,0.000185227,0.000232105,0.00001353155,0.0001765913,0.0008651054],"genre_scores_gemma":[0.3833953,0.00007602218,0.6162142,0.0001249314,0.00002526868,0.00001145578,0.0000597895,0.00002516536,0.0000678738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3730013,"threshold_uncertainty_score":0.7007308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0131666924372091,"score_gpt":0.3475365771738309,"score_spread":0.3343698847366218,"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."}}