{"id":"W2896233414","doi":"10.1109/group4.2018.8478722","title":"Automating Photonic Design with Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Solver; Computer science; Artificial neural network; MATLAB; Nonlinear system; Photonics; Parametric statistics; Finite-difference time-domain method; Grating; Range (aeronautics); Electronic engineering; Algorithm; Computational science; Artificial intelligence; Engineering; Optics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006405154,0.0005391422,0.0003982165,0.000243987,0.0003200712,0.0008665791,0.001046474,0.0007210603,0.002578417],"category_scores_gemma":[0.001586701,0.000443322,0.0004422155,0.0002088966,0.0004749176,0.001165402,0.0007660157,0.001166796,0.0008000973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006801371,"about_ca_system_score_gemma":0.001098591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001345774,"about_ca_topic_score_gemma":0.002804732,"domain_scores_codex":[0.9996526,0.00007036103,0.00001320491,0.00006876075,0.000158724,0.00003626908],"domain_scores_gemma":[0.9994067,0.0002623373,0.00005823041,0.0001717456,0.00008369344,0.00001738471],"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":[0.00005686069,0.0001107089,0.0008013053,0.0001017834,0.00004278796,0.0000586897,0.00006907927,0.8092641,0.01979932,0.009524703,0.001804727,0.1583659],"study_design_scores_gemma":[0.000002950578,0.00001360092,0.00004466281,0.000003624769,0.000002417377,0.00001116567,0.000003362763,0.9891489,0.006573448,0.003032336,0.001159406,0.000004208079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02361056,0.00009958354,0.9693766,0.0001769753,0.00002830516,0.00003659762,0.00005101809,0.002349786,0.004270607],"genre_scores_gemma":[0.3496414,0.0001483188,0.6462415,0.0001598336,0.00001881667,0.0001197339,0.000154316,0.0002593878,0.003256784],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002578417,"threshold_uncertainty_score":0.008625627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832202539061403,"score_gpt":0.2262440749081126,"score_spread":0.2079220495174986,"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."}}