{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001167908,0.0001669968,0.000198739,0.00005715453,0.0001873283,0.00005370292,0.0001462222,0.00005063595,0.0002003733],"category_scores_gemma":[0.00001452027,0.0001650525,0.00004766453,0.0000885158,0.00004423505,0.00006068837,0.00003973789,0.0002256497,0.000008670825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005005256,"about_ca_system_score_gemma":0.00005146892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003076227,"about_ca_topic_score_gemma":0.000004335317,"domain_scores_codex":[0.9991087,0.000023998,0.0001446863,0.0001996554,0.0001318307,0.0003911371],"domain_scores_gemma":[0.9995725,0.0001146728,0.00002311831,0.000142542,0.00002768444,0.0001195149],"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":[0.002027006,0.0008981556,0.0001923798,0.001354721,0.0009813092,0.0001062207,0.002355742,0.08586942,0.2341524,0.5733958,0.06638692,0.03227995],"study_design_scores_gemma":[0.0007574626,0.000811604,0.00003355213,0.00001458542,0.00006066199,0.000005906902,0.0003539364,0.9651306,0.0219932,0.004955771,0.005556432,0.0003263126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.924881,0.0006355785,0.03665085,0.0006736988,0.000595962,0.001090206,0.0001238283,0.0004267806,0.03492204],"genre_scores_gemma":[0.9960548,0.0002848445,0.002156192,0.0002687284,0.00004034017,0.0001425883,0.00002404462,0.00002831748,0.00100017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8792611,"threshold_uncertainty_score":0.6730644,"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."}}