{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004438698,0.0005517054,0.0003883594,0.0005508531,0.0003556815,0.0007483656,0.0009725494,0.001076722,0.002159952],"category_scores_gemma":[0.001761674,0.0004374053,0.0005480066,0.0004883943,0.0005976104,0.0008462145,0.0008569068,0.0009545598,0.0003740917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007504756,"about_ca_system_score_gemma":0.0008306448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004370511,"about_ca_topic_score_gemma":0.00480216,"domain_scores_codex":[0.9998258,0.0000431454,0.000009211241,0.00002938515,0.00007702599,0.00001551109],"domain_scores_gemma":[0.9995748,0.0002420664,0.00004288593,0.00005600681,0.00006706169,0.0000171495],"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":[0.00001608635,0.00002362238,0.0002871052,0.00004169585,0.00001715428,0.00003094816,0.00002434288,0.9735343,0.00254588,0.009294624,0.0003490946,0.01383515],"study_design_scores_gemma":[0.000001085207,0.000002057117,0.00002637093,0.000001971172,7.908445e-7,0.00000473505,0.000001521115,0.9981428,0.0003270141,0.001255711,0.0002341448,0.000001782428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01022313,0.0001381827,0.985037,0.0001287995,0.00004315875,0.00003161892,0.00007252651,0.0003702633,0.00395519],"genre_scores_gemma":[0.4708214,0.0003914691,0.5206678,0.0002255854,0.00005792658,0.0002809247,0.0002756873,0.0001954625,0.007083785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004370511,"threshold_uncertainty_score":0.008690119,"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."}}