{"id":"W3206356753","doi":"10.1098/rspa.2022.0162","title":"System norm regularization methods for Koopman operator approximation","year":2022,"lang":"en","type":"preprint","venue":"Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Office of Naval Research; National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; Toyota Research Institute","keywords":"Mathematics; Weighting; Operator (biology); Applied mathematics; Norm (philosophy); Regularization (linguistics); Linear matrix inequality; Operator norm; Convex optimization; Matrix norm; Regular polygon; Mathematical optimization; Operator theory; Mathematical analysis; Computer science; Eigenvalues and eigenvectors","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":[0.00236021,0.001515203,0.0009702505,0.0008155995,0.0004872867,0.0013018,0.001337816,0.001633024,0.003569317],"category_scores_gemma":[0.00478499,0.0005747021,0.001150907,0.0006788439,0.00147046,0.001554529,0.002192637,0.003398715,0.001148227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063125,"about_ca_system_score_gemma":0.001772347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003881432,"about_ca_topic_score_gemma":0.003559807,"domain_scores_codex":[0.9989993,0.0004081212,0.00005272485,0.0001367383,0.0003384967,0.00006466221],"domain_scores_gemma":[0.9983367,0.0009951118,0.0001328266,0.0001595777,0.0003102363,0.00006555986],"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":[0.0001028216,0.0000721547,0.0005316066,0.0003672578,0.00009240369,0.00010919,0.0002055071,0.6130207,0.009315655,0.2910247,0.004472773,0.08068515],"study_design_scores_gemma":[0.0000035097,0.00001209266,0.00003476095,0.0000121265,0.000003019909,0.00001045618,0.000009301071,0.9804195,0.0006932973,0.01699647,0.001798768,0.000006843414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001129262,0.000134552,0.997362,0.00009691111,0.00002724982,0.0000193361,0.00002219347,0.00006662846,0.001141807],"genre_scores_gemma":[0.1525799,0.001280421,0.8298495,0.0003693092,0.0002130883,0.0006028166,0.0004886691,0.0006071207,0.01400917],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003881432,"threshold_uncertainty_score":0.01248211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679088491136291,"score_gpt":0.2762847356221088,"score_spread":0.2594938507107458,"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."}}