{"id":"W2798028937","doi":"10.1016/j.jcp.2018.06.026","title":"KIOPS: A fast adaptive Krylov subspace solver for exponential integrators","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Numerical methods for differential equations","field":"Mathematics","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"GLS Industries (Canada)","funders":"National Science Foundation","keywords":"Krylov subspace; Orthogonalization; Solver; Jacobian matrix and determinant; Matrix exponential; Exponential function; Exponential integrator; Mathematics; Generalized minimal residual method; Applied mathematics; Linear system; Integrator; Subspace topology; Algorithm; A priori and a posteriori; Matrix (chemical analysis); Mathematical optimization; Computer science; Iterative method; Mathematical analysis; Differential equation","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":[0.0005854886,0.0007420764,0.0006647707,0.0003689435,0.0004102135,0.0006960352,0.001518476,0.0007733222,0.003952594],"category_scores_gemma":[0.001599547,0.0003614598,0.0005056083,0.0004988165,0.0006087318,0.001105354,0.001668164,0.001596335,0.00123443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003479621,"about_ca_system_score_gemma":0.001598279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002242454,"about_ca_topic_score_gemma":0.002668052,"domain_scores_codex":[0.9997033,0.00006321896,0.00001788183,0.00002987693,0.0001498973,0.00003570818],"domain_scores_gemma":[0.9995272,0.0001820582,0.00004037391,0.00006660407,0.0001296135,0.00005404637],"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":[0.0002522869,0.0001376165,0.0009557097,0.0002831772,0.00008714661,0.0001668052,0.0001881374,0.715379,0.02044781,0.06515376,0.007079375,0.1898692],"study_design_scores_gemma":[0.00002149655,0.00001881677,0.00003665779,0.000005601372,0.000002544451,0.00001900771,0.00000941405,0.993172,0.001465764,0.003039594,0.002202219,0.00000681077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004975221,0.00006648117,0.9928253,0.00006169814,0.00003549296,0.00003862908,0.00004348124,0.0006377298,0.001316024],"genre_scores_gemma":[0.1201894,0.000191885,0.8744422,0.0001004734,0.00004428068,0.0002161115,0.0002582885,0.0006378733,0.003919536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003952594,"threshold_uncertainty_score":0.01322275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0973643129976356,"score_gpt":0.387755753497511,"score_spread":0.2903914404998754,"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."}}