{"id":"W3025465415","doi":"10.1364/osac.413394","title":"Successive training of a generative adversarial network for the design of an optical cloak","year":2020,"lang":"en","type":"preprint","venue":"OSA Continuum","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; European Research Council","keywords":"Cloak; Cloaking; Shell (structure); Generative grammar; Generative adversarial network; Convolution (computer science); Computer science; Algorithm; Artificial intelligence; Metamaterial; Deep learning; Engineering; Optics; Mechanical engineering; Physics; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008767859,0.0007840243,0.000544981,0.0002794877,0.000215822,0.0003967312,0.0008194002,0.001248582,0.001627075],"category_scores_gemma":[0.002517606,0.0004658645,0.0004671383,0.0001977414,0.0009904918,0.0005596306,0.001018542,0.001209459,0.000285842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007459859,"about_ca_system_score_gemma":0.0005481617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002984662,"about_ca_topic_score_gemma":0.003768383,"domain_scores_codex":[0.9997539,0.00009440316,0.000007197465,0.00005519785,0.00004825663,0.00004110022],"domain_scores_gemma":[0.999128,0.0006347631,0.00006294892,0.00005262408,0.00007894049,0.00004262084],"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":[0.00004434048,0.00001783687,0.0002763882,0.00001299523,0.0000113603,0.00003757587,0.00002310957,0.9859129,0.001894919,0.002461303,0.0002727618,0.009034565],"study_design_scores_gemma":[0.000001216626,0.000004974137,0.00001347412,8.838598e-7,8.4846e-7,0.000002728252,0.000001010802,0.9992844,0.0002070198,0.0004503682,0.00003233942,8.112185e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08795352,0.0002369626,0.9069698,0.0005513447,0.00004206031,0.0000354107,0.00004023421,0.0005356116,0.003635046],"genre_scores_gemma":[0.9129583,0.00007386172,0.08321898,0.0002487433,0.00001921524,0.00008563388,0.00009106207,0.00007580051,0.00322827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002984662,"threshold_uncertainty_score":0.005934596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.104190141214418,"score_gpt":0.3161933082928063,"score_spread":0.2120031670783882,"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."}}