{"id":"W4316021906","doi":"10.1109/jmmct.2023.3236946","title":"Electromagnetic-Thermal Analysis With FDTD and Physics-Informed Neural Networks","year":2023,"lang":"en","type":"article","venue":"IEEE journal on multiscale and multiphysics computational techniques","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multiphysics; Finite-difference time-domain method; Solver; Artificial neural network; Finite difference method; Finite element method; Computer science; Electromagnetic field; Interfacing; Applied mathematics; Boundary value problem; Physics; Computational science; Mathematics; Mathematical optimization; Mathematical analysis; Artificial intelligence; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001382302,0.000230393,0.0003161771,0.0001910478,0.0001713888,0.0001092965,0.00008072194,0.00007231365,0.000005793067],"category_scores_gemma":[0.00001107387,0.000190804,0.00008944645,0.0008264133,0.00008401216,0.0001761808,0.00001451628,0.0003789266,0.000001972592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003316665,"about_ca_system_score_gemma":0.00001306143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002985147,"about_ca_topic_score_gemma":0.000001690166,"domain_scores_codex":[0.9989092,0.00006275513,0.0002724123,0.0001872339,0.0002627663,0.0003056209],"domain_scores_gemma":[0.9990878,0.000486788,0.00007946958,0.00008938727,0.00009693469,0.0001596221],"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":[0.00004457634,0.00002886606,0.001240987,0.00001336492,0.0002082887,0.00001234675,0.00009777385,0.7437496,0.003139789,0.0001443401,0.0001233645,0.2511967],"study_design_scores_gemma":[0.0004535676,0.0004675382,0.03035764,0.0000207866,0.0001104916,0.00002354891,0.000009616949,0.9654122,0.001856851,0.0009578465,0.00008810232,0.0002418109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6383212,0.00007646813,0.3606592,0.00008991543,0.00006180648,0.0001641043,0.000004671928,0.0004431336,0.0001795294],"genre_scores_gemma":[0.935497,0.0002084353,0.0637941,0.0001455422,0.0002475164,0.00001866066,0.00002033931,0.00003499255,0.0000334397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2971758,"threshold_uncertainty_score":0.7780763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01200151005701243,"score_gpt":0.2752895958607328,"score_spread":0.2632880858037203,"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."}}