{"id":"W4386147854","doi":"10.1109/pn58661.2023.10222959","title":"Machine Learning-Enabled Inverse Design of Subwavelength Waveguide Gratings using Environment-Aware Effective Medium Approximations","year":2023,"lang":"en","type":"article","venue":"","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Inverse; Grating; Computer science; Waveguide; Artificial neural network; Inverse problem; Surrogate model; Optics; Artificial intelligence; Physics; Machine learning; 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":[0.000338903,0.0003799233,0.0004941283,0.0001985394,0.0001829672,0.0006492461,0.0006108476,0.0007452303,0.001010212],"category_scores_gemma":[0.0007402199,0.000368167,0.0004061292,0.0001766693,0.0005864914,0.0006546729,0.0004158312,0.0007228798,0.0002205742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006434749,"about_ca_system_score_gemma":0.0009734181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001597413,"about_ca_topic_score_gemma":0.002733788,"domain_scores_codex":[0.9998872,0.00002780707,0.000003558364,0.0000119837,0.00005287367,0.00001644888],"domain_scores_gemma":[0.9997322,0.0001414606,0.00003555039,0.00003543119,0.00003518803,0.00002019074],"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":[0.00001543443,0.0000193004,0.0001669324,0.00002033174,0.000009737557,0.00002886946,0.00001703032,0.9865906,0.003964184,0.005725789,0.000137666,0.003304018],"study_design_scores_gemma":[0.000001707676,0.00000298314,0.000009326728,8.278921e-7,3.645677e-7,0.000002299511,0.00000110087,0.9990231,0.0003557142,0.0005197811,0.00008197481,8.20473e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1096114,0.0001908303,0.87863,0.00034751,0.00003396302,0.00004757923,0.0001053648,0.0004711301,0.01056222],"genre_scores_gemma":[0.7128785,0.0001372173,0.2838585,0.0001017207,0.00001129047,0.000135207,0.0001257562,0.0001343578,0.00261748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001597413,"threshold_uncertainty_score":0.004668772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01742608558212465,"score_gpt":0.2172673820117711,"score_spread":0.1998412964296465,"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."}}