{"id":"W4290709601","doi":"10.1364/cleo_qels.2022.fm5h.4","title":"Minimal memory differentiable FDTD for inverse design","year":2022,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bottleneck; Finite-difference time-domain method; Differentiable function; Construct (python library); Computer science; Inverse; Mode (computer interface); Algorithm; Parallel computing; Mathematics; Embedded system; Programming language; Mathematical analysis; Physics; Optics","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":[0.0001619695,0.0003216978,0.0002227394,0.0001790957,0.0001626381,0.0003730975,0.000421594,0.0003320125,0.002976559],"category_scores_gemma":[0.0005052824,0.0001386207,0.0002021435,0.0001492834,0.0003451003,0.0003626715,0.0003244928,0.0006054759,0.0006869062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003989933,"about_ca_system_score_gemma":0.0003172422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004491651,"about_ca_topic_score_gemma":0.0007495686,"domain_scores_codex":[0.9999269,0.00001418679,0.000002588441,0.00000826007,0.00004095488,0.000006936731],"domain_scores_gemma":[0.9998935,0.00004287302,0.00001022183,0.00002816265,0.00001980124,0.00000540778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001639596,0.00008744001,0.0005290883,0.0003031289,0.00003052042,0.0001439655,0.0001677831,0.2668786,0.1327843,0.4033398,0.005652175,0.1899192],"study_design_scores_gemma":[0.00001769607,0.00003846725,0.00005968747,0.00001232227,0.000006441957,0.00005274182,0.000007834476,0.9457492,0.02037861,0.0245259,0.009143421,0.00000763414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005506518,0.00006201668,0.9889423,0.00009552176,0.00002001046,0.000009207336,0.00002604138,0.0002678859,0.005070484],"genre_scores_gemma":[0.2496472,0.0001368817,0.7448449,0.00007951351,0.00001496476,0.00007117893,0.00008502744,0.0001371209,0.004983118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002976559,"threshold_uncertainty_score":0.009957612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02529003509921258,"score_gpt":0.2208483178207801,"score_spread":0.1955582827215675,"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."}}