{"id":"W4387031753","doi":"10.48550/arxiv.2309.13022","title":"Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Finite element method; Embedding; Artificial neural network; Computer science; Parametric statistics; Graph; Graph embedding; Representation (politics); Algorithm; Vertex (graph theory); Theoretical computer science; Artificial intelligence; Structural engineering; Mathematics; 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":[0.0002913381,0.0006702192,0.0003295245,0.0004665567,0.0001858922,0.000392946,0.0004794576,0.0008008121,0.001417723],"category_scores_gemma":[0.0009595032,0.0002490192,0.0003586234,0.0003880537,0.0003309128,0.000701748,0.00031269,0.0005847218,0.0002033975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005027051,"about_ca_system_score_gemma":0.000385974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006901812,"about_ca_topic_score_gemma":0.007922721,"domain_scores_codex":[0.9999113,0.00002713333,0.000003380446,0.00002373942,0.00002284762,0.00001157976],"domain_scores_gemma":[0.9997179,0.0001675614,0.00003441252,0.00001925888,0.00004723949,0.00001362841],"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":[0.00002030091,0.000009425947,0.0002563823,0.000006253516,0.000003679382,0.0000109414,0.000004490351,0.9942564,0.0005069749,0.0003487977,0.00009964423,0.00447671],"study_design_scores_gemma":[2.541698e-7,0.000001939024,0.00004651054,3.94612e-7,3.318754e-7,5.316581e-7,8.079056e-7,0.9996839,0.00007999956,0.0001744476,0.00001027942,5.470479e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4316247,0.0004729977,0.561116,0.0004670159,0.00007491929,0.00004186678,0.0003700781,0.0010434,0.004788926],"genre_scores_gemma":[0.9816379,0.0001215772,0.0165816,0.00003610672,0.000009620257,0.00002773456,0.0001935207,0.00003863679,0.001353341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006901812,"threshold_uncertainty_score":0.01372325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06032111131672696,"score_gpt":0.1877149699940098,"score_spread":0.1273938586772828,"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."}}