{"id":"W4392538982","doi":"10.48550/arxiv.2403.03199","title":"Operator Learning Renormalization Group","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science; Alliance de recherche numérique du Canada; National Science Foundation; Government of Canada; U.S. Department of Energy; Ministero dello Sviluppo Economico; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Operator (biology); Renormalization group; Group (periodic table); Mathematics; Mathematical physics; Physics; Quantum mechanics","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":[0.001392231,0.0007348554,0.0008933984,0.0006007132,0.0008940997,0.001097934,0.002008477,0.001540475,0.004042349],"category_scores_gemma":[0.004552668,0.0003459288,0.0009900328,0.0003685543,0.002668503,0.002695975,0.001607532,0.001941867,0.0006593016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001058044,"about_ca_system_score_gemma":0.0008395147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001084976,"about_ca_topic_score_gemma":0.0008047704,"domain_scores_codex":[0.9993666,0.0002891457,0.00001745317,0.00009311149,0.0001894702,0.00004418299],"domain_scores_gemma":[0.9990169,0.0004465726,0.00006672368,0.0002912153,0.0001096814,0.00006893311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002143367,0.00004143349,0.0001978749,0.00005401586,0.00002792603,0.00005004234,0.0000676963,0.128775,0.001744112,0.8577798,0.001082169,0.01015861],"study_design_scores_gemma":[0.000009287276,0.00001892661,0.00004553792,0.000006213183,0.000003276267,0.00001880875,0.000005664516,0.639003,0.000420999,0.3588882,0.001570077,0.00001010023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01486091,0.0002941402,0.9700178,0.0009014373,0.0001890636,0.00007205624,0.00004841148,0.0003876512,0.01322843],"genre_scores_gemma":[0.6207622,0.0007786023,0.3625646,0.001090587,0.0005010882,0.0005822015,0.0001813452,0.0004822373,0.01305719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004042349,"threshold_uncertainty_score":0.01352304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04834290818501676,"score_gpt":0.1798484466271308,"score_spread":0.1315055384421141,"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."}}