{"id":"W4405745687","doi":"10.1088/2632-2153/ada33b","title":"Asymptotically stable data-driven koopman operator approximation with inputs using total extended DMD","year":2024,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Toyota Research Institute; Office of Naval Research Global; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Operator (biology); Applied mathematics; Mathematics; Stability (learning theory); Stability theory; Control theory (sociology); Computer science; Physics; Artificial intelligence; Biology; Nonlinear system","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":[0.001148639,0.0008982093,0.0007555853,0.0005939017,0.0002968974,0.0006309171,0.0009245913,0.0007097229,0.001390851],"category_scores_gemma":[0.002275306,0.0003451319,0.0008815852,0.000431829,0.0005551337,0.000774023,0.001031002,0.001146252,0.0004615524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004995703,"about_ca_system_score_gemma":0.001155901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005101682,"about_ca_topic_score_gemma":0.003937043,"domain_scores_codex":[0.9995998,0.0001452065,0.00002401712,0.00008662304,0.0001088066,0.00003558985],"domain_scores_gemma":[0.9993285,0.0003447625,0.00008311113,0.00007486536,0.0001377489,0.00003109658],"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":[0.000103231,0.00004511353,0.0006365824,0.0001218286,0.0000480461,0.00006795893,0.0000936798,0.8949867,0.007640023,0.0109633,0.0009307431,0.08436277],"study_design_scores_gemma":[0.00000171941,0.000007172241,0.00003444049,0.000002258127,0.000001111944,0.000003584399,0.000003038088,0.9984579,0.000471596,0.0008784517,0.0001367711,0.000001857835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01255518,0.0000732806,0.9866036,0.00004700871,0.00001296462,0.00001820617,0.00003992919,0.0002376831,0.0004122638],"genre_scores_gemma":[0.4969844,0.000226926,0.4976299,0.00009629149,0.00004235983,0.0002057216,0.0006213422,0.0001685574,0.00402461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005101682,"threshold_uncertainty_score":0.010144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548296919706542,"score_gpt":0.2737657493641807,"score_spread":0.2582827801671153,"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."}}