{"id":"W4413134912","doi":"10.1145/3759245","title":"Extending <tt>Irksome</tt> : Improvements in Automated Runge–Kutta Time Stepping for Finite Element Methods","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Mathematical Software","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Runge–Kutta methods; Solver; Computer science; Finite element method; Partial differential equation; Applied mathematics; Diagonal; Computational science; Mathematical optimization; Algorithm; Differential equation; Mathematics; Programming language; Mathematical analysis; Geometry","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":[0.002282641,0.001416402,0.001013755,0.001033888,0.000761011,0.001793049,0.003744549,0.001323695,0.0227932],"category_scores_gemma":[0.006928083,0.0009636883,0.002028402,0.001165831,0.0008516739,0.002757984,0.002748022,0.003288996,0.01419558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007571093,"about_ca_system_score_gemma":0.002167279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004005864,"about_ca_topic_score_gemma":0.005570892,"domain_scores_codex":[0.9974768,0.0004376475,0.0002366059,0.0002379775,0.001399145,0.0002118231],"domain_scores_gemma":[0.9968565,0.00082294,0.0002065442,0.001010907,0.0009984341,0.0001047572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004089214,0.0003644748,0.001972103,0.001035544,0.0001402431,0.0005020417,0.000651497,0.1895751,0.04632097,0.1013389,0.1243462,0.5333441],"study_design_scores_gemma":[0.0001380126,0.00009975281,0.0006860897,0.0001553991,0.00003355594,0.0003331896,0.00004018932,0.6560068,0.03723443,0.01762028,0.2874963,0.0001559551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00335828,0.0001824066,0.9430993,0.0001447521,0.0001627569,0.00009484297,0.0007649324,0.04135671,0.01083598],"genre_scores_gemma":[0.04205957,0.0003944856,0.9195412,0.0002361022,0.0001399399,0.0003610167,0.003292664,0.02302227,0.01095281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0227932,"threshold_uncertainty_score":0.07625085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02712013469491828,"score_gpt":0.3757368847493364,"score_spread":0.3486167500544181,"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."}}