{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008841064,0.0001915773,0.0003073803,0.00001366709,0.0001383793,0.00004983349,0.0001339345,0.000004263944,0.0002752898],"category_scores_gemma":[0.000006148395,0.0001438333,0.0001289757,0.0002364224,0.0001083919,0.0001393958,0.00003750555,0.0001724583,0.0003639071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001622714,"about_ca_system_score_gemma":0.00002063407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004424767,"about_ca_topic_score_gemma":0.000002829123,"domain_scores_codex":[0.9990265,0.00006208609,0.0001440008,0.0002839409,0.0002197662,0.0002637394],"domain_scores_gemma":[0.9994035,0.00007902828,0.0001321363,0.0002190406,0.00008690928,0.00007932219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001204993,0.001098214,0.1731661,0.001275877,0.001130573,0.00001756429,0.002177404,0.0002027705,0.4300915,0.09136317,0.03546216,0.2638941],"study_design_scores_gemma":[0.005044882,0.002064561,0.04581695,0.01110729,0.00188862,0.000007207082,0.0004233872,0.01891516,0.1381643,0.007245737,0.7639639,0.005358013],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910986,0.0004270843,0.001657433,0.002820195,0.00006361704,0.000336051,0.000003799934,0.00005734929,0.003535846],"genre_scores_gemma":[0.996286,0.00003962986,0.0001028955,0.002153223,0.001216779,0.00004336101,0.00002256714,0.00002473378,0.0001107864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7285018,"threshold_uncertainty_score":0.5865351,"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."}}