{"id":"W2770251224","doi":"10.1103/physrevlett.121.032501","title":"Eigenvector Continuation with Subspace Learning","year":2018,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Cold Atom Physics and Bose-Einstein Condensates","field":"Physics and Astronomy","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Air Force Research Laboratory; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Air Force Office of Scientific Research; Forschungszentrum Jülich; National Energy Research Scientific Computing Center; U.S. Department of Transportation; Advanced Research Projects Agency; College of Engineering, Michigan State University; Army Research Laboratory; Ontario Ministry of Research, Innovation and Science; Office of Naval Research; North Carolina State University; National Science Foundation; Michigan State University; U.S. Department of Energy","keywords":"Eigenvalues and eigenvectors; Hamiltonian (control theory); Hamiltonian matrix; Linear algebra; Subspace topology; Applied mathematics; Diagonalizable matrix; Stiefel manifold; Generalized eigenvector; Mathematics; Vector space; Manifold (fluid mechanics); Pure mathematics; Symmetric matrix; Mathematical analysis; Physics; Mathematical optimization; Quantum mechanics; State-transition matrix","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":[0.001379819,0.001001508,0.001074352,0.0008639984,0.0008224271,0.001315562,0.001033522,0.00141801,0.004867533],"category_scores_gemma":[0.005642323,0.0005125453,0.0007560822,0.0009006162,0.0013737,0.001976683,0.002105088,0.002195426,0.00211987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006537548,"about_ca_system_score_gemma":0.001420365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205675,"about_ca_topic_score_gemma":0.00233403,"domain_scores_codex":[0.9994815,0.0002491076,0.00002134673,0.0000847222,0.0001143482,0.00004896349],"domain_scores_gemma":[0.9983824,0.0009572931,0.0000958244,0.0002382851,0.0002213337,0.000104776],"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":[0.0001882681,0.0001462282,0.001147997,0.0001648288,0.00007695812,0.0001441496,0.0002188708,0.5186917,0.003658011,0.2198362,0.008484068,0.2472426],"study_design_scores_gemma":[0.00001120408,0.00002021043,0.00005581208,0.00001003413,0.000003058765,0.00001450977,0.00001436481,0.9061505,0.0005954337,0.09147781,0.001637808,0.00000916938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007868889,0.000240362,0.9880484,0.0002881284,0.0000666628,0.00003530878,0.00004852218,0.0006370391,0.002766653],"genre_scores_gemma":[0.2925862,0.0005818037,0.6938841,0.0003255652,0.0002267118,0.0002926254,0.0004758784,0.0005678186,0.01105928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004867533,"threshold_uncertainty_score":0.01628351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00938218681095121,"score_gpt":0.2643723531519254,"score_spread":0.2549901663409742,"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."}}