{"id":"W2061450341","doi":"10.1016/j.compstruc.2011.10.004","title":"An isoparametric approach to level set topology optimization using a body-fitted finite-element mesh","year":2011,"lang":"en","type":"article","venue":"Computers & Structures","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Topology optimization; Finite element method; Topology (electrical circuits); Mathematics; Set (abstract data type); Level set method; Level set (data structures); Mathematical optimization; Mixed finite element method; Applied mathematics; Computer science; Structural engineering; Engineering; Combinatorics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001463022,0.0006134809,0.00127776,0.001392351,0.0005671427,0.00123296,0.002417583,0.001771672,0.003992794],"category_scores_gemma":[0.00474674,0.001054678,0.0009645444,0.001171662,0.001078623,0.001339958,0.002042872,0.002277602,0.001228797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006663082,"about_ca_system_score_gemma":0.0008956288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001132641,"about_ca_topic_score_gemma":0.001360131,"domain_scores_codex":[0.999271,0.0002480175,0.00003264485,0.00007597065,0.0003457047,0.00002668897],"domain_scores_gemma":[0.9991435,0.0004405611,0.000062664,0.0001172646,0.0001986846,0.00003737801],"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":[0.00005013968,0.00009621876,0.0002310661,0.0001007411,0.00002863534,0.00005728393,0.0000892924,0.8520561,0.005681056,0.06732731,0.001141848,0.07314017],"study_design_scores_gemma":[0.000003698491,0.00001050594,0.00001729832,0.00000431909,0.000002599341,0.00001106195,0.000005248296,0.9917241,0.0004180746,0.007161686,0.0006370406,0.000004452285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001245179,0.00001634445,0.9978114,0.00003410894,0.00001313665,0.00002286265,0.00001063764,0.00006377288,0.0007826131],"genre_scores_gemma":[0.07040744,0.00009928656,0.9266617,0.00008571787,0.00002978108,0.0002205335,0.00009797601,0.00025901,0.002138554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003992794,"threshold_uncertainty_score":0.01335722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04521471012604145,"score_gpt":0.2535312470605643,"score_spread":0.2083165369345228,"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."}}