{"id":"W4409176062","doi":"10.1016/j.cma.2025.117921","title":"A graph neural network surrogate model for multi-objective fluid-acoustic shape optimization","year":2025,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Surrogate model; Shape optimization; Artificial neural network; Computer science; Mathematical optimization; Mathematics; Finite element method; Artificial intelligence; Engineering; Structural engineering","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":[0.0006005458,0.0008190419,0.0005355439,0.0004564501,0.0002851648,0.000612905,0.0009490488,0.001263425,0.001693332],"category_scores_gemma":[0.002015969,0.0003775046,0.0006389404,0.0004401527,0.0005457547,0.0007962381,0.0007860299,0.00103187,0.0003605409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007275427,"about_ca_system_score_gemma":0.0008701531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006266656,"about_ca_topic_score_gemma":0.005233233,"domain_scores_codex":[0.9997402,0.00008956015,0.00001081424,0.00004570081,0.00008471237,0.00002892387],"domain_scores_gemma":[0.9994879,0.0002602094,0.00005307242,0.00003181202,0.0001389717,0.00002807048],"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":[0.00000778833,0.00000543989,0.0001348274,0.000009339715,0.000004795593,0.00001291707,0.000004768609,0.9955042,0.000349697,0.001324641,0.000121062,0.002520485],"study_design_scores_gemma":[5.851179e-7,0.00000259601,0.00001399335,9.889202e-7,5.230437e-7,0.000001648762,6.917253e-7,0.9994998,0.00005279988,0.0003618978,0.00006380931,7.01456e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02445357,0.0002480961,0.9694778,0.0002565453,0.00006411523,0.00003581226,0.0001290884,0.0002574831,0.00507759],"genre_scores_gemma":[0.8481467,0.0003487066,0.143473,0.0002433214,0.00004709055,0.0003100353,0.0005220746,0.0001394257,0.006769564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006266656,"threshold_uncertainty_score":0.01246035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536446486024928,"score_gpt":0.3291871364225147,"score_spread":0.2938226715622654,"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."}}