{"id":"W4210642503","doi":"10.1115/imece2021-72861","title":"A Comparative Study Between a Sharp and a Diffuse Topology Optimization Method for Thermal Problems","year":2021,"lang":"en","type":"article","venue":"","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Regularization (linguistics); Topology optimization; Robustness (evolution); Parametrization (atmospheric modeling); Topology (electrical circuits); Level set method; Mathematical optimization; Mathematics; Conductor; Thermal; Applied mathematics; Algorithm; Computer science; Finite element method; Physics; Geometry; Optics; Image (mathematics); Artificial intelligence; Image segmentation","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":[0.003246172,0.0009355396,0.001186098,0.001095575,0.0003128081,0.001201654,0.001115225,0.001533945,0.001662992],"category_scores_gemma":[0.005728356,0.0004644806,0.0009849314,0.000687439,0.00089863,0.001044286,0.001420076,0.001360148,0.00031674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00050919,"about_ca_system_score_gemma":0.0008174002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001268193,"about_ca_topic_score_gemma":0.001262917,"domain_scores_codex":[0.9991707,0.0004438059,0.00003599577,0.0000717066,0.0002333052,0.00004449159],"domain_scores_gemma":[0.9965582,0.002433771,0.0001452447,0.000275021,0.0004298573,0.000157778],"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":[0.0002827137,0.00019268,0.0009301614,0.0003874885,0.00009771816,0.00006775165,0.0001139848,0.9084956,0.008282579,0.01956427,0.0005534458,0.0610316],"study_design_scores_gemma":[0.00001321951,0.0001303387,0.0001851289,0.00001538664,0.00001037609,0.00002382548,0.00001431296,0.9968972,0.001096346,0.001230424,0.0003749046,0.000008527135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08571183,0.0008910668,0.9077882,0.0002379129,0.00006063826,0.0001090067,0.00006588866,0.0003579687,0.004777522],"genre_scores_gemma":[0.5467806,0.0006810549,0.4494903,0.0001323313,0.00004308297,0.0002106285,0.0001900882,0.0002867184,0.002185165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003246172,"threshold_uncertainty_score":0.01716763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02881452911050053,"score_gpt":0.2970343381515902,"score_spread":0.2682198090410897,"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."}}