{"id":"W1999841373","doi":"10.1007/s00158-014-1186-8","title":"Differential geometry tools for multidisciplinary design optimization, Part I: Theory","year":2014,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Cambridge Trust; Cambridge Commonwealth Trust","keywords":"Multidisciplinary design optimization; Mathematical proof; Multidisciplinary approach; Differentiable function; Sensitivity (control systems); Limit (mathematics); Computer science; Function (biology); Differential (mechanical device); Engineering design process; Riemannian geometry; Translation (biology); Information geometry; Mathematics; Algebra over a field; Mathematical optimization; Geometry; Pure mathematics; Engineering; Mathematical analysis; Curvature; Mechanical engineering; Aerospace engineering","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":[0.001150656,0.001302865,0.00156855,0.002012959,0.0005411039,0.001727506,0.001459387,0.0009941563,0.004863548],"category_scores_gemma":[0.002527317,0.0007112504,0.001220964,0.001870497,0.001768964,0.001752529,0.002147831,0.002622176,0.001465786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326078,"about_ca_system_score_gemma":0.0006751817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008701395,"about_ca_topic_score_gemma":0.0008830135,"domain_scores_codex":[0.9991962,0.0003119114,0.00003804547,0.00007952895,0.0003408428,0.00003350797],"domain_scores_gemma":[0.9992239,0.0004043674,0.00006892629,0.0001193918,0.000146811,0.00003657137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001287117,0.00004219409,0.0002294544,0.0003068967,0.00005357054,0.00005801771,0.0001076125,0.1302661,0.002440362,0.7821872,0.006314307,0.07798143],"study_design_scores_gemma":[0.0000116957,0.00002932252,0.0002049625,0.0000780449,0.00002026236,0.00008178147,0.00003792029,0.446549,0.001126937,0.5229399,0.02890289,0.00001729662],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000865775,0.002399231,0.9908254,0.0002900429,0.0001294188,0.00001623015,0.0000439233,0.00005781472,0.005372191],"genre_scores_gemma":[0.2113559,0.01064213,0.7554436,0.0007346378,0.000924162,0.0005165154,0.0003402037,0.000463677,0.01957912],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004863548,"threshold_uncertainty_score":0.01627022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.021270214954356,"score_gpt":0.2709732141313832,"score_spread":0.2497029991770272,"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."}}