{"id":"W3126913540","doi":"10.48550/arxiv.2102.03613","title":"Linear Matrix Inequality Approaches to Koopman Operator Approximation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Linear matrix inequality; Mathematics; Regularization (linguistics); Convex optimization; Applied mathematics; Operator (biology); Mathematical optimization; Matrix (chemical analysis); Linear regression; Regular polygon; Computer science; Statistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002721009,0.001710183,0.001252239,0.001065833,0.0004573412,0.002006909,0.002037806,0.001593821,0.004737399],"category_scores_gemma":[0.006665881,0.0005848734,0.001122701,0.001518541,0.002114852,0.002195098,0.001938671,0.004318508,0.001435808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001474909,"about_ca_system_score_gemma":0.001247777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0032263,"about_ca_topic_score_gemma":0.002299771,"domain_scores_codex":[0.9980519,0.0009752428,0.00008641899,0.0002727347,0.0005147169,0.00009907693],"domain_scores_gemma":[0.9970505,0.002092505,0.0002359144,0.0001828934,0.0003821228,0.00005595005],"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":[0.00003771496,0.00004845565,0.0001620541,0.0003006178,0.00008681479,0.0001108907,0.0001051331,0.2932941,0.001955888,0.6688661,0.003731418,0.03130089],"study_design_scores_gemma":[0.00000578812,0.00002022186,0.00006108989,0.00003509767,0.0000112254,0.00002301059,0.00001524977,0.7512431,0.0005072982,0.2444243,0.003641185,0.00001249434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008851317,0.00150101,0.9905093,0.0004122358,0.00007799153,0.00001429974,0.00004656123,0.00005743999,0.00649611],"genre_scores_gemma":[0.3771604,0.01152189,0.5784553,0.0009878022,0.00152009,0.0006096815,0.0006126428,0.0004889434,0.02864335],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004737399,"threshold_uncertainty_score":0.01584822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2297636472030142,"score_gpt":0.2257436019507823,"score_spread":0.004020045252231874,"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."}}